Recent lbnl_cs_amcs items

Fast Sparse Matrix Permutation for Mesh-Based Direct Solvers

Wed, 26 Aug 2026 00:00:00 +0000

We present a fast sparse matrix permutation algorithm tailored to linear systems arising from triangle meshes. Our approach produces nested-dissection-style permutations while significantly reducing permutation runtime overhead. Rather than enforcing strict balance and separator optimality, the algorithm deliberately relaxes these design decisions to favor fast partitioning and efficient elimination-tree construction. Our method decomposes permutation into patch-level local orderings and a compact quotient-graph ordering of separators, preserving the essential structure required by sparse Cholesky factorization while avoiding its most expensive components. We integrate our algorithm into vendor-maintained sparse Cholesky solvers on both CPUs and GPUs. Across a range of graphics applications, including single factorizations and repeated factorizations, our method reduces permutation time and improves the sparse Cholesky solve performance by up to 6.27 ×. Our code is available at...

Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data

Mon, 24 Aug 2026 00:00:00 +0000

Zero-shot and prompt-based models have excelled at visual reasoning tasks by leveraging large-scale natural image corpora, but they often fail on sparse and domain-specific scientific image data. We introduce Zenesis, a no-code interactive computer vision platform designed to reduce data readiness bottlenecks in scientific imaging workflows. Zenesis integrates lightweight multimodal adaptation for zero-shot inference on raw scientific data, human-in-the-loop refinement, and heuristic-based temporal enhancement. We validate our approach on Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets of catalyst-loaded membranes. Zenesis outperforms baselines, achieving an average accuracy of 0.947, Intersection over Union (IoU) of 0.858, and Dice score of 0.923 on amorphous catalyst samples; and 0.987 accuracy, 0.857 IoU, and 0.923 Dice on crystalline samples. These results represent a significant performance gain over conventional methods such as Otsu thresholding and standalone...

How Frequent Will the Rarest Daily Rainfall Records of Hurricane Ida’s Remnants Be in the Future?

Fri, 21 Aug 2026 00:00:00 +0000

Abstract Gaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary...

Snow-eater heat waves of the western United States

Thu, 20 Aug 2026 00:00:00 +0000

Abrupt snowmelt, triggered by rain-on-snow events or "snow-eater heat waves," can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850-2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates,...

Noise-aware optimization in nominally identical manufacturing and measuring systems for high-throughput parallel workflows

Tue, 18 Aug 2026 00:00:00 +0000

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or enforce generic robustness, the proposed framework explicitly determines whether shared optimization across devices is appropriate based on the degree of inter-device noise heterogeneity. This enables improved performance, reproducibility,...

Transformer-based operator learning framework for self-energy in strongly correlated systems

Tue, 18 Aug 2026 00:00:00 +0000

We introduce Σ-Attention, a transformer-based operator-learning framework for approximating the self-energy operator of strongly correlated electronic systems. By creating a batched dataset that combines results from three complementary approaches, i.e., many-body perturbation theory, strong-coupling expansion, and exact diagonalization, each effective in specific parameter regimes, Σ-Attention is applied to learn an accurate approximation for the self-energy operator that is valid across a wide range of parameter regimes. This hybrid strategy leverages the strengths of existing methods while relying on the transformer's ability to generalize beyond individual limitations. More importantly, the scalability of the transformer architecture allows the learned self-energy to be extended to systems with larger sizes, leading to much improved computational scaling. Using the one-dimensional Hubbard model, we demonstrate that Σ-Attention can accurately predict the Matsubara Green's function...

Linear complexity

Tue, 18 Aug 2026 00:00:00 +0000

We present factorization and solution phases for a new linear complexity direct solver designed for concurrent batch operations on fine-grained parallel architectures, for matrices amenable to hierarchical representation. We focus on the strong-admissibility-based $\mathscr{H}^{2}$ format, where strong recursive skeletonization factorization compresses remote interactions. We build upon previous implementations of $\mathscr{H}^{2}$ matrix construction for efficient factorization and solution algorithm design, which are illustrated graphically in stepwise detail. The algorithms are ‘blackbox’ in the sense that the only inputs are the matrix and right-hand side, without analytical or geometrical information about the origin of the system. We demonstrate linear complexity scaling in both time and memory on four representative families of dense matrices up to one million in size. Parallel scaling up to 16 threads is enabled by a multi-level matrix graph coloring and avoidance of dynamic...

Observation of Synchronization between Two Quantum van der Pol Oscillators in Trapped Ions

Tue, 18 Aug 2026 00:00:00 +0000

Synchronization is a hallmark of collective behavior that emerges when nonlinear systems interact, spanning scales from mechanical oscillators to planetary orbits. As a universal phenomenon, it underpins the study of complex systems and has far-reaching technological implications. While classical synchronization has a long and rich history, it has not been observed experimentally between multiple quantum limit-cycle oscillators despite a decade of theoretical investigations. We realize synchronization between two quantum van der Pol oscillators by engineering dissipation in a mixed-isotope trapped-ion quantum simulator. The synchronized state is encoded in a fixed relative phase between the oscillators that is inaccessible to individual measurements and revealed only through joint readout of both oscillators, in stark contrast to the system in the (deterministic) classical limit where synchronization can be observed via individual phase measurements. We further show that the relative...

Towards a Verifiable Domain-Specific Language for Hardware-Accelerated Stencils

Thu, 30 Jul 2026 00:00:00 +0000

Defining a domain-specific language (DSL) that supports vector-calculus abstractions eases the porting of partial differential equation (PDE) solvers to specialized architectures. Sufficiently high-level abstractions empower users to express universal laws with sufficient generality that the laws must always hold true within their domain of validity. A broad class of PDE solvers employs stencil-based algorithms, the target domain of Berkeley Lab's stencil accelerator chip co-design project. First released as open-source in January 2026, the Formal software framework lays a foundation for defining an embedded DSL based on composable operators that implement mimetic numerical methods -- stencil algorithms that guarantee satisfaction of discrete versions of important vector calculus theorems. The Formal DSL will be the frontend to a new class of stencil-PDE accelerators developed jointly by LBNL, UHCL, and UC Berkeley through the DOE Competitive Portfolios for Computer Science Project....

The Persistent Challenge of Data Locality in the Post-Exascale Era

Thu, 30 Jul 2026 00:00:00 +0000

The era of exascale computing, exemplified by systems like Frontier achieving exaflop-level performance, marks a milestone. However, the quest for sheer compute power leads to strong imbalance in system design. Hence, scaling advancements in memory, network bandwidth, and storage are also necessary and pose challenges, with a crucial need to address data locality issues. This article underscores the fundamental importance of data locality as a key abstraction for optimizing application performance. Despite notable software solutions, the growing complexity of parallelism and memory hierarchy demands performance-portable data locality solutions across diverse computing platforms. The article revisits data locality aspects, covering hardware considerations, application perspectives, software stack abstractions, and tool support. It concludes with insights into data locality challenges and opportunities, emphasizing the ongoing significance of collaborative research for progress...

Fast solvers for tokamak fluid models with PETSc

Tue, 28 Jul 2026 00:00:00 +0000

Multigrid (MG) is widely recognized as a highly effective solver for the model problem, the Laplacian, but textbook MG fails on most problems of interest. MG methods have been applied to complex, real-world applications with careful consideration of the physical model and discretization. This work develops the first step in applying MG methods to science and engineering relevant magnetohydrodynamics (MHD) tokamak models in the M3D-C1 (https://m3dc1.pppl.gov) fusion energy science code. The semi-implicit time integrator in M3D-C1 is composed of many linear solves. The implicit advance of the momentum equation is the most challenging and is the focus of this work. The current production solver in M3D-C1 is a block Jacobi (BJ) preconditioner within a Krylov solver, where blocks group degrees of freedom on planes of constant toroidal coordinate. BJ convergence degrades as the number of planes increases due to the spectral properties of the matrix preconditioned with BJ. The partially...

Ab initio many-fermion structure calculations on a quantum computer

Wed, 22 Jul 2026 00:00:00 +0000

To overcome the limitations of existing algorithms for solving self-bound quantum many-body problems—such as those encountered in nuclear and particle physics—that access only a restricted subset of energy levels and provide limited structural information, we introduce and demonstrate a novel quantum-classical approach capable of resolving the complete bound-state spectrum. This method also provides the total angular momentum J associated with each eigenstate. Our approach is based on expressing the Hamiltonian in second-quantized form within a novel input model combined with a scan scheme, enabling broad applicability to configuration-interaction calculations across diverse fields. We apply this hybrid method to compute, for the first time, the bound-state spectrum together with corresponding J values of O20 using a realistic strong-interaction Hamiltonian. Our approach applies to hadron spectra and J values solved in the relativistic basis light-front quantization approach.

Polynomial-time preparation of low-temperature Gibbs states for two-dimensional toric code

Tue, 21 Jul 2026 00:00:00 +0000

We propose a polynomial-time algorithm for preparing the Gibbs state of the two-dimensional toric code Hamiltonian at any temperature, starting from any initial state, significantly improving upon prior estimates that suggested exponential scaling with inverse temperature. We prove that fast mixing at low temperature for the two-dimensional toric code can be achieved by augmenting local jump operators with simple global jump operators, which enable efficient transitions between logical sectors. To establish tight lower bounds on the spectral gap, we introduce a new reduction method that eventually maps the problem to estimating the spectral gap of a perturbed graph Laplacian on a stair graph. Our proof also shows that the Lindblad dynamics with a digitally implemented low-temperature local Davies generator is able to efficiently drive the quantum state toward the ground state manifold.

Efficient Tensor Completion Algorithms for Highly Oscillatory Operators

Wed, 15 Jul 2026 00:00:00 +0000

We address the problem of recovering highly oscillatory operators, represented as (Formula presented.) matrices with a fixed set of observed entries. Given that these matrices can be well compressed by butterfly matrix decomposition of (Formula presented.) levels requiring only (Formula presented.) degrees of freedom, we propose a novel reformulation of the butterfly structure as a compact tensor network. Specifically, we reshape the input matrix as an order (Formula presented.) dense tensor, and cast its butterfly decomposition as a tensor network consisting of order (Formula presented.) dense tensors. This enables efficient utilization of the existing software infrastructure for dense and sparse tensor computations. Next, we propose several tensor completion algorithms based on the tensor reformulation of butterfly format, and compare them against algorithms using the quantized tensor train (QTT) format. These algorithms leverage popular completion methods such as alternating...

A sharp interface method for two-phase incompressible flow with surface tension

Wed, 15 Jul 2026 00:00:00 +0000

We present a sharp-interface method for resolved two-phase incompressible viscous flow based on embedded boundary finite volume discretizations of the individual phase domains. The numerical algorithm is a fractional step method, in which incompressibility is enforced at the half-step and the full-step by solving coupled Hodge projections that respect the jump boundary conditions in pressure and pressure gradient at the interface. Surface tension enters through these boundary conditions. The viscous term in the momentum equations is solved implicitly using a Crank-Nicholson time discretization, respecting the jump conditions on velocity and velocity gradient. The method is implemented with block-structured adaptive mesh refinement. We demonstrate stable behavior in 2D and 3D, with the velocity converging in all norms as measured using Richardson extrapolation. The method successfully models spherical cap bubble shapes and velocities, showing good agreement with a range of experiments.

Toward Unified Autonomous Scattering Experiments: A Cross-Facility Case Study at ALS and PETRA III

Wed, 1 Jul 2026 00:00:00 +0000

Abstract Autonomous experiments rely on the integration of control, data acquisition, analysis, and decision-making frameworks. While such systems have been demonstrated at individual facilities, adapting them to additional instruments remains challenging due to differences in local infrastructure. We present a modular workflow that connects existing open-source tools for data access (Tiled), workflow orchestration (Prefect), analysis and visualization (pyFAI, Plotly Dash), and Gaussian-process-based adaptive sampling (gpCAM) into a unified framework for autonomous scattering experiments. The same configuration operates across two synchrotron beamlines (ALS 7.3.3 and PETRA III P03) with only minimal facility-specific adjustments, as shown in proof-of-concept demonstrations. This validates that a consistent design emphasizing modularity and shared interfaces can ease deployment across diverse experimental environments. The resulting framework provides a flexible foundation for...

Optimized Auxiliary Functions for Robust Mitigation of Finite-Size Errors in Periodic Hybrid Density Functional Theory

Wed, 1 Jul 2026 00:00:00 +0000

When calculating properties of periodic systems at the thermodynamic limit (TDL), the dominant source of finite size error (FSE) arises from the long-range Coulomb interaction, and can manifest as a slowly converging quadrature error when approximating an integral in the reciprocal space by a finite sum. The singularity subtraction (SS) method offers a systematic approach for reducing this quadrature error and thus the FSE. In this work, we first investigate the performance of the SS method in the simplest setting, aiming at reducing the FSE in exact exchange calculations by subtracting the Coulomb contribution with a single, adjustable Gaussian auxiliary function. We demonstrate that a simple fitting method can robustly estimate the optimal Gaussian width and leads to rapid convergence toward the TDL. Furthermore, we suggest new forms of the auxiliary function, whose optimal parameters could also be determined through least-squares fitting. For a range of semiconductors and insulators,...

Existence and Regularity Results for a Nonlinear Fluid-Structure Interaction Problem with Three-Dimensional Structural Displacement

Wed, 1 Jul 2026 00:00:00 +0000

In this paper we investigate a nonlinear fluid-structure interaction (FSI) problem involving the Navier-Stokes equations, that describe the flow of an incompressible, viscous fluid in a 3D domain interacting with a thin viscoelastic lateral wall. The wall's elastodynamics is modeled by a two-dimensional plate equation with fractional damping, accounting for displacement in all three directions. The system is nonlinearly coupled through kinematic and dynamic conditions imposed at the time-varying fluid-structure interface, whose location is not known a priori. We establish three key results, particularly significant for FSI problems that account for vector displacements of thin structures. Specifically, we first establish a hidden spatial regularity for the structure displace ment, which forms the basis for proving that self-contact of the structure will not occur within a finite time interval. Second, we demonstrate temporal regularity for both the structure and fluid velocities,...

Operator-level quantum acceleration of non-logconcave sampling

Mon, 29 Jun 2026 00:00:00 +0000

Sampling from probability distributions of the form [Formula: see text], where [Formula: see text] is a continuous potential, is a fundamental task across physics, chemistry, biology, computer science, and statistics. However, when [Formula: see text] is nonconvex, the resulting distribution becomes non-logconcave, and classical methods such as Langevin dynamics often exhibit poor performance. We introduce a quantum algorithm that provably accelerates a broad class of continuous-time sampling dynamics. For Langevin dynamics, our method encodes the target Gibbs measure into the amplitudes of a quantum state, identified as the kernel of a block matrix derived from a factorization of the Witten Laplacian operator. This connection enables Gibbs sampling via singular value thresholding and yields up to a quartic quantum speedup over best-known classical Langevin-based methods in the non-logconcave setting. Building on this framework, we further develop the first quantum algorithm that...

ReMU: regional minimal updating for model-based derivative-free optimization

Thu, 25 Jun 2026 00:00:00 +0000

Derivative-free optimization (DFO) problems are optimization problems where derivative information is unavailable or extremely difficult to obtain. Model-based DFO solvers have been applied extensively in scientific computing. Powell's NEWUOA (2004) [Powell, The NEWUOA software for unconstrained optimization without derivatives, in Large-Scale Nonlinear Optimization, Nonconvex Optimization and its Applications Vol. 83, G. Di Pillo and M. Roma, eds., Springer, 2006, pp. 255–297] and Wild's POUNDerS (2014) [Wild, Solving derivative-free nonlinear least squares problems with POUNDERS, in Advances and Trends in Optimization with Engineering Applications, T. Terlaky, M.F. Anjos, and S. Ahmed, eds., SIAM, 2017, pp. 529–540] explore the numerical power of the minimal norm Hessian (MNH) model for DFO and contributed to the open discussion on building better models with fewer data to achieve faster numerical convergence. Another decade later, we propose the regional minimal updating (ReMU)...

A multimodal large language model for materials science

Thu, 25 Jun 2026 00:00:00 +0000

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence...

Classification of dynamical Lie algebras generated by spin interactions on undirected graphs

Fri, 19 Jun 2026 00:00:00 +0000

Dynamical Lie algebras (DLAs) are a versatile tool for various topics that span from the expressibility-trainability of variational quantum algorithms (VQAs), to simulation of many body Hamiltonians. Quantum gates and most of the Hamiltonians of interest consist of local interactions; therefore, the analysis of all possible DLAs generated by 1- and 2-local operators is crucial for quantum simulation and VQAs on current hardware. Previously in [R. Wiersema et al., npj Quantum Inf. 10, 110 (2024)], we analyzed the DLAs on linear, circular and all-to-all topologies, and obtained results about their dimensions and algebraic structure. In this work, we extend our analysis into any possible hardware topology and provide a classification of all DLAs generated by Pauli strings on any undirected interaction graph. Our results indicate that the DLAs depend solely on whether the connectivity or interaction graph is bipartite or not. In addition, we find that the non-trivial polynomially...

Verification of an energy-conserving semi-implicit electrostatic particle-in-cell scheme for modeling high-density plasma at scale

Fri, 19 Jun 2026 00:00:00 +0000

A verification study of a semi-implicit energy-conserving electrostatic particle-in-cell algorithm is presented. The algorithm relaxes the time-step and mesh-size constraints that require resolution of the plasma period and Debye length associated with traditional explicit momentum-conserving particle-in-cell algorithms. Physical implications and applicability of using the semi-implicit scheme for modeling high-density plasmas are discussed. Where possible, numerical results are compared against analytical solutions. The simulation results indicate that the algorithm is stable at time steps larger than twice the inverse plasma frequency and cell sizes larger than the Debye length. It is found that the algorithm gives adequate results, provided that the distribution function and the spatiotemporal scales dictating the physics of the problem are resolved. As such, the algorithm may provide a robust method for kinetic modeling of high-density plasmas at scale.

Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining

Fri, 19 Jun 2026 00:00:00 +0000

CRISPR-Cas9-based lineage tracing technologies have enabled the reconstruction of single-cell phylogenies from transcriptional readouts. However, developing tree-reconstruction algorithms with theoretical guarantees in this setting is challenging. In this work, we derive a reconstruction algorithm with theoretical guarantees using Neighbor-Joining (NJ) on distances that are moment-matched to estimate the true tree distances. We develop a series of tools to analyze this algorithm and prove its theoretical guarantees. When the parameters of the data generating process are known and there is no missing data, our results align with established results from common evolutionary models, such as Cavender-Farris-Neyman and Jukes-Cantor. However, to account for the realistic case where the parameters of the data generating process are not known and there is missing data, we develop new theory that shows for the first time that it is still possible to obtain reconstruction guarantees in...

Edits for UTI 002: collective subroutine sequencing

Thu, 18 Jun 2026 00:00:00 +0000

This paper contains Fortran 202Y specification edits to explain and resolve problems with work item DIN1: Collectives over a specified team. It replaces critical sequencing requirements in the Fortran standard that govern the correctness of collective subroutine invocations. It passed by unanimous consent at the Jun 2026 meeting #240 of the INCITS/US Fortran Programming Language Standards Technical Committee.

thornado+FLASH-X: A Hybrid Discontinuous Galerkin–Implicit-explicit and Finite-volume Framework for Neutrino-radiation Hydrodynamics in Core-collapse Supernovae

Wed, 10 Jun 2026 00:00:00 +0000

We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado) and their coupling to self-gravitating hydrodynamics within the adaptive mesh refinement–based multiphysics simulation framework FLASH-X. thornado, developed primarily for simulations of core-collapse supernovae (CCSNe), employs a spectral, six-species two-moment formulation with algebraic closure and special-relativistic observer corrections accurate to O(v/c) , and uses discontinuous Galerkin (DG) methods for phase-space discretization combined with implicit-explicit time stepping. A key development is a nonlinear neutrino–matter coupling algorithm based on nested fixed-point iteration with Anderson acceleration, enabling fully implicit treatment of collisional processes, including energy-coupling interactions such as neutrino–electron scattering and pair production. Coupling to finite-volume (FV) hydrodynamics is achieved through a hybrid DG-FV representation...

Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra

Wed, 10 Jun 2026 00:00:00 +0000

Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is #BQP-complete in the worst case. In this work, we introduce QFAMES (quantum filtering and analysis of multiplicities in eigenvalue spectra), a quantum algorithm that efficiently identifies clusters of closely spaced dominant eigenvalues and determines their multiplicities under physically motivated assumptions, which allows us to bypass worst-case complexity barriers. QFAMES also enables the estimation of observable expectation values within targeted energy clusters, providing a powerful tool for studying quantum phase transitions and other physical properties. We validate the effectiveness of QFAMES through numerical demonstrations, including its applications to characterizing...

Multiscale analysis of large twist ferroelectricity and swirling dislocations in bilayer hexagonal boron nitride

Thu, 4 Jun 2026 00:00:00 +0000

With its atomically thin structure and intrinsic ferroelectric properties, heterodeformed bilayer hexagonal boron nitride (hBN) has gained prominence in next-generation non-volatile memory applications. However, studies to date have focused almost exclusively on small-twist bilayer hBN, leaving the question of whether ferroelectricity can persist under small heterostrain and large heterodeformation entirely unexplored. In this work, we establish the crystallographic origin of ferroelectricity in bilayer hBN configurations heterodeformed relative to high-symmetry configurations such as AA-stacking and 21.786789° twisted configurations (Σ7), using Smith normal form bicrystallography. We then demonstrate out-of-plane ferroelectricity in bilayer hBN across configurations vicinal to both the AA and Σ7 stackings. Atomistic simulations reveal that AA-vicinal systems support ferroelectricity under both small twist and small strain, with polarization switching in the latter...

Dirac Fermions and Flat Bands in Phosphorus Carbide Nanotubes: Structural and Quantum Phase Transitions in a Quasi-One-Dimensional Material

Thu, 4 Jun 2026 00:00:00 +0000

Chemically realistic quasi-one-dimensional (1D) materials in which Dirac Fermions and highly degenerate flat bands coexist intrinsically at the Fermi level are exceedingly rare, while representing a highly desirable platform for correlated and topological quantum phenomena. Here, using specialized symmetry-adapted first-principles calculations we predict a new class of nanomaterials─phosphorus carbide nanotubes (P₂C₃NTs)─obtained by rolling monolayer P₂C₃, a two-dimensional material shown in a previous letter to host "double Kagome bands". Both armchair and zigzag P₂C₃NTs are stable at room temperature and feature the rare coexistence of Dirac crossings and multiple flat bands at the Fermi level inherited from the underlying honeycomb-Kagome lattice, with the flat bands resilient to elastic deformations. Under large strain, the structure transforms from honeycomb-Kagome to "brick-wall", accompanied by multiple coupled...

Electronic structure prediction of medium and high entropy alloys across composition space

Thu, 4 Jun 2026 00:00:00 +0000

We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our...

Compact representation and long-time extrapolation of real-time data for quantum systems using the ESPRIT algorithm

Wed, 27 May 2026 00:00:00 +0000

Representing real-time data as a sum of complex exponentials provides a compact form that enables both denoising and extrapolation. As a fully data-driven method, the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is agnostic to the underlying physical equations, making it broadly applicable to various observables and experimental or numerical setups. In this work, we consider applications of the ESPRIT algorithm primarily to extend real-time dynamical data from simulations of quantum systems. We evaluate ESPRIT's performance in the presence of noise and compare it to other extrapolation methods. We demonstrate its ability to extract information from short-time dynamics to reliably predict long-time behavior and determine the minimum time interval required for accurate results. We discuss how this insight can be leveraged in numerical methods that propagate quantum systems in time, and we show how ESPRIT can predict infinite-time values...

AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases

Thu, 21 May 2026 00:00:00 +0000

We present AstraAI, a command-line interface (CLI) coding framework for high-performance computing (HPC) software development. AstraAI operates directly within a Linux terminal and integrates large language models (LLMs) with Retrieval-Augmented Generation (RAG) and Abstract Syntax Tree (AST)-based structural analysis to enable context-aware code generation for complex scientific codebases. The central idea is to construct a high-fidelity prompt that is passed to the LLM for inference. This prompt augments the user request with relevant code snippets retrieved from the underlying framework codebase via RAG and structural context extracted from AST analysis, providing the model with precise information about relevant functions, data structures, and overall code organization. The framework is designed to perform scoped modifications to source code while preserving structural consistency with the surrounding code. AstraAI supports both locally hosted models from Hugging Face and...

Parallel Runtime Interface for Fortran (PRIF) Specification, Revision 0.8

Tue, 19 May 2026 00:00:00 +0000

This document specifies an interface to support the multi-image parallelism features of Fortran, named the Parallel Runtime Interface for Fortran (PRIF). PRIF is a solution in which a runtime library is primarily responsible for implementing coarray allocation, deallocation and accesses, image synchronization, atomic operations, events, teams and collective subroutines. The Fortran compiler is responsible for transforming the invocation of Fortran-level multi-image parallelism features into procedure calls to the necessary PRIF subroutines. The interface is designed for portability across shared- and distributed-memory machines, different operating systems, and multiple architectures. Implementations of this interface are intended as an augmentation for the compiler's own runtime library. With an implementation-agnostic interface, alternative parallel runtime libraries may be developed that support the same interface. One benefit of this approach is the ability to vary the communication...

Reverse segregation and self-organization in inclined chute flows of bidisperse granular mixtures

Fri, 15 May 2026 00:00:00 +0000

In the usual segregation scenario for stable inclined chute flows of bidisperse mixtures of fine and coarse spherical particles, coarse particles rise toward the free surface, forming a coarse-rich region atop the flowing pile. Beyond a threshold coarse-to-fine diameter ratio of approximately 4, conversely, the weight of the coarse particles exceeds the segregation driving forces, causing individual coarse particles to sink within the pile and producing a reversed segregation state. However, an understanding of the collective evolution of the pile structure is still lacking when the particle diameter ratio exceeds 4 and the coarse-particle mass fraction is appreciable. To explore this broadly bidisperse limit, we perform discrete element method simulations considering mean particle diameter ratios of up to 8 and coarse-particle mass fractions spanning 0.1 to 0.9. The steady-state flow profiles reveal several intriguing behaviors that depend on the diameter ratio and mass fraction....

Real-time estimators for scattering observables: A full account of finite-volume errors for quantum simulation

Thu, 14 May 2026 00:00:00 +0000

The real-time correlators of quantum field theories can be directly probed through new approaches to simulation, such as quantum computing and tensor networks. This provides a new framework for computing scattering observables in lattice formulations of strongly interacting theories, such as lattice quantum chromodynamics. In this paper, we prove that the proposal of real-time estimators of scattering observables is universally applicable to all scattering observables of gapped quantum field theories. All finite-volume errors are exponentially suppressed, and the rate of this suppression is controlled by the regulator considered, namely, a displacement of the spectrum of the theory into the complex plane. A partial restoration of Lorentz symmetry by averaging over different boosts gives an additional suppression of finite volume errors. Our results also apply to the simulation of wave packet scattering, where a similar averaging is performed to construct the wave packets that...

A Fast Algorithm for Computing Zigzag Representatives

Thu, 14 May 2026 00:00:00 +0000

Zigzag filtrations of simplicial complexes generalize the usual filtrations by allowing simplex deletions in addition to simplex insertions. The barcodes computed from zigzag filtrations encode the evolution of homological features. Although one can locate a particular feature at any index in the filtration using existing algorithms, the resulting representatives may not be compatible with the zigzag: a representative cycle at one index may not map into a representative cycle at its neighbor. For this, one needs to compute compatible representative cycles along each bar in the barcode. It is known that the barcode for a zigzag filtration with m insertions and deletions can be computed in O(mω)$$O(m^\omega )$$ time, where ω<2.373$$\omega < 2.373$$ is the matrix multiplication exponent. However, it is not known how to compute the compatible representatives so efficiently. For a non-zigzag filtration, the classical matrix-based algorithm provides representatives in O(m3)$$O(m^3)$$...

Quantifying Interconnect Energy Efficiency on Perlmutter: pJ/bit Measurements of NVLink, PCIe, Slingshot NICs, and Rosetta Switches

Wed, 13 May 2026 00:00:00 +0000

In the exascale era, comprehensive energy accounting is critical for sustainable HPC. Standard monitoring captures CPU and GPU power, but the energy footprint of interconnects, including switches, NICs, and PCIe/NVLinks, remains hidden due to limited hardware sensors. We address this with a software-centric methodology using targeted microbenchmarks on the Perlmutter (HPE Cray EX) system at NERSC. By correlating controlled stress on specific network components with job- and rack-level power telemetry, we isolate each component’s energy consumption, quantifying dynamic energy per bit (pJ/bit) for active communication while separating constant-power overhead. Integrating network bandwidth measurements from vendor tools such as NVIDIA DCGM and CrayPat enables fine-grained, application-level estimates of network energy use, unattainable with standard monitoring. This approach establishes a generalizable framework for evaluating network energy, providing actionable insights for designing...

A Flexible Forwarding Scheme to Improve Latency-Bound Irregular P2P Communication in MPI

Wed, 13 May 2026 00:00:00 +0000

We propose an algorithm to efficiently perform latency-bound communication scenarios that consist of many small messages. In these parallel scenarios, processes typically pass around a lot of small-sized messages of a few KBs of size. Performing communication operations with P2P MPI routines or collective MPI routines (including neighborhood collectives) in such scenarios may not always yield the optimal results and may not resolve the latency bottleneck. To this end, we develop a regular structure called virtual process topology (VPT) on which the messages can be communicated in a structured and controlled manner. Using parameters of this topology, one can tune the rate of aggression in tackling the latency costs. We demonstrate that our communication algorithm is preferable to MPI P2P and collective routines for latency-bound communication and it can easily be adapted only by replacing calls to MPI routines in a parallel application. We show how to adapt existing topology-aware...

Absorption dissymmetry factor enhancement: A data-driven approach to unravel the synthesis knobs of chiral 2D perovskites

Thu, 7 May 2026 00:00:00 +0000

Chiral 2D metal halide perovskites (MHPs) are promising for spin-optoelectronic applications, yet their absorption dissymmetry factor (g abs ) exhibits significant variability due to complex, co-dependent structural and experimental factors. We established a data-driven framework using Pearson’s correlation, ANOVA, and Gaussian process regression to identify and model key synthesis “knobs” governing these properties. The analysis revealed that solvent choice is the primary factor driving variability. For acetonitrile-based films, g abs was maximized by optimizing annealing temperature and film thickness. Conversely, films from higher boiling point solvents showed complex dependencies on annealing temperature, excitonic integral intensity, and film texture. These statistical correlations provide a roadmap for the rational design of high-performance chiral MHPs and establish a foundation for future machine learning-driven material exploration.

Data Release 1 of the Dark Energy Spectroscopic Instrument

Tue, 5 May 2026 00:00:00 +0000

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5 yr spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the Universe between z = 0 and z ≈ 4. DESI’s principal scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI DR1, which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data, which were previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled....

Idiomatic Vibe Testing with Julienne

Thu, 30 Apr 2026 00:00:00 +0000

Historically, the role of natural language in programs was confined primarily to comments that have no direct influence on runtime behavior. With the rise of vibe coding, natural language becomes central to code generation via prompt engineering with a large language model (LLM). One fundamental problem, however, lies in natural language’s inherent ambiguity. By contrast, standardized programming languages greatly reduce ambiguity by formally defining syntax and specifying detailed semantics in a written language standard. To leverage such formality and specifications in vibe coding, a user might consider replacing or augmenting natural language with code. If the prompt includes unit tests, then the tests serve both as instructions for what the LLM-generated code must do and a tool for verifying that the generated code accomplishes the desired task. This tutorial unifies the above two themes by enabling users to write unit tests that take the form of natural language using specific...

Rates of Sea‐Level Rise Are Highly Sensitive to Ice Viscosity Parameters in Model Benchmarks

Fri, 24 Apr 2026 00:00:00 +0000

Abstract Glacier flow plays a major role in current and future rates of globally averaged sea‐level rise. The viscosity of glacial ice, controlling the rate of flow, decreases as stress increases and is highly sensitive to the value of the stress exponent, , in the constitutive equation for viscous flow. Glaciologists and climate modelers almost exclusively assume when modeling ice flow and projecting sea‐level rise through forward modeling. However, recent work suggests that better fits observations, prompting the question: How sensitive are projections of sea‐level rise to the value of ? We use an established community ice flow model and standard benchmark experiments designed as an idealized representation of Pine Island Glacier, West Antarctica. While initializing an model to match observations of an ice sheet is possible, we find that incorrectly assuming when in fact dramatically underestimates rates of sea‐level rise. The scale of this error grows nonlinearly with the magnitude...

Thermodynamically consistent incorporation of the Langmuir adsorption model into compressible fluctuating hydrodynamics

Fri, 24 Apr 2026 00:00:00 +0000

For a gas-solid interfacial system where chemical species undergo reversible adsorption, we develop a mesoscopic stochastic modeling method that simulates both gas-phase hydrodynamics and surface coverage dynamics by coupling the Langmuir adsorption model with compressible fluctuating hydrodynamics. To this end, we derive a thermodynamically consistent mass-energy update scheme that accounts for how the mass and energy variables in the gas and surface subsystems should be updated according to the changes in the number of molecules of each species in each subsystem due to adsorption and desorption events. By performing a stochastic analysis for the ideal Langmuir model and the full hydrodynamic system, we analytically confirm that our mass-energy update scheme captures thermodynamic equilibrium predicted by equilibrium statistical mechanics. We find that an internal energy correction term is needed, which is attributed to the difference in the mean kinetic energy of gas molecules...

Coupled Lindblad Pseudomode Theory for Simulating Open Quantum Systems

Fri, 24 Apr 2026 00:00:00 +0000

Coupled Lindblad pseudomode theory is a promising approach for simulating non-Markovian quantum dynamics on both classical and quantum platforms, with dynamics that can be realized as a quantum channel. We provide theoretical evidence that the number of coupled pseudomodes only needs to scale as polylog(T/ϵ) in the simulation time T and precision ϵ. Inspired by the realization problem in control theory, we also develop a robust numerical algorithm for constructing the coupled modes that avoid the nonconvex optimization required by existing approaches. We demonstrate the effectiveness of our method by computing population dynamics and absorption spectra for the spin-boson model. This Letter provides a significant theoretical and computational improvement to the coupled Lindblad framework, which impacts a broad range of applications from classical simulations of quantum impurity problems to quantum simulations on near-term quantum platforms.

Quantum Computing and Visualization Research Challenges and Opportunities

Wed, 22 Apr 2026 00:00:00 +0000

Quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.

Foundation models for atomistic simulation of chemistry and materials

Wed, 22 Apr 2026 00:00:00 +0000

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on...

Automated ICRF heating surrogate modeling via machine learning

Wed, 22 Apr 2026 00:00:00 +0000

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models....

NUPACK: Computational Nucleic Acid Analysis and Design

Tue, 21 Apr 2026 00:00:00 +0000

NUPACK is a growing software suite for the analysis and design of nucleic acid structures, devices, and systems serving the needs of researchers in the fields of nucleic acid nanotechnology, molecular programming, synthetic biology, and across the life sciences. NUPACK algorithms have pioneered the treatment of complex and test tube ensembles containing arbitrary numbers of interacting strand species, providing crucial tools for capturing concentration effects essential to analyzing and designing the intermolecular interactions that are a hallmark of these fields. Analysis and design of multitube ensembles enable reaction pathway engineering of dynamic hybridization cascades and structural engineering including the possibility of pseudoknots. The all-new NUPACK 4 scientific code base offers enhanced physical models (coaxial and dangle stacking subensembles), dramatic speedups (20-120× for test tube analysis), increased scalability for large complexes (e.g., 30,000 nt), mixed materials...

Laplace Transform–Based Quantum Eigenvalue Transformation via Linear Combination of Hamiltonian Simulation

Mon, 6 Apr 2026 00:00:00 +0000

Laplace Transform–Based Quantum Eigenvalue Transformation via Linear Combination of Hamiltonian Simulation

A projection method for particle resampling

Thu, 2 Apr 2026 00:00:00 +0000

Particle discretizations of partial differential equations are advantageous for high-dimensional kinetic models in phase-space due to their better scalability than continuum approaches with respect to dimension. Complex processes collectively referred to as particle noise hamper long time simulations with particle methods. One approach to address this problem is particle mesh adaptivity, or remapping, known as particle resampling and remeshing. This work introduces a resampling method that projects particles to and from a (finite element) function space. The method is simple, using standard sparse linear algebra and finite element techniques, and it preserves all moments up to the order of a polynomial represented exactly by the continuum function space. It is distinguished from most other mesh-based methods in that new particle positions and number are decoupled from the mesh, allowing particle and continuum meshes to be adapted relatively independently. While this work is developed...

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Tue, 31 Mar 2026 00:00:00 +0000

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation...

Structure preservation using discrete gradients in the Vlasov-Poisson-Landau system

Tue, 31 Mar 2026 00:00:00 +0000

We present a novel structure-preserving framework for solving the Vlasov-Poisson-Landau system of equations using a particle in cell (PIC) discretization combined with discrete gradient time integrators. The Vlasov-Poisson-Landau system is an accurate model for studying hot plasma dynamics at a kinetic scale where small-angle Coulomb collisions dominate. Our scheme guarantees conservation of mass, momentum and energy as well as preservation of the monotonicity of entropy production in both the time-continuous and discrete systems. We employ the conservative integrator for both the Hamiltonian Vlasov-Poisson equations and the dissipative Landau equation using the PETSc library (www.mcs.anl.gov/petsc) to showcase structure-preserving properties.

Ab Initio Bulk Free Energy Surface of Proper Ferroelectrics

Tue, 31 Mar 2026 00:00:00 +0000

We report a systematic and accurate approach for deriving the bulk free energy surface (FES), a function of temperature, polarization, and strain, from the first-principles density functional theory (DFT) of proper ferroelectrics. The core of our approach is the metadynamics algorithm that extracts the polarization dependence of the FES from all-atom molecular dynamics simulations without an a priori ansatz. The rest of the FES is derived from the metadynamics trajectories that span the relevant phase space. We demonstrate our approach in the case of lead titanate. The errors across the phase transition, due to DFT numerics, all-atom molecular dynamics, and free energy evaluation by enhanced sampling, can be systematically controlled and are of the order of 1 meV/atom. The accuracy of the resulting ab initio FES is only limited by the adopted functional approximation of DFT.

Rapid Quantum Ground State Preparation via Dissipative Dynamics

Wed, 25 Mar 2026 00:00:00 +0000

Inspired by natural cooling processes, dissipation has become a promising approach for preparing low-energy states of quantum systems. However, the potential of dissipative protocols remains unclear beyond certain commuting Hamiltonians. This work provides significant analytical and numerical insights into the power of dissipation for preparing the ground state of noncommuting Hamiltonians. For quasi-free dissipative dynamics, including certain 1D spin systems with boundary dissipation, our results reveal a new connection between the mixing time in trace distance and the spectral properties of a non-Hermitian Hamiltonian, leading to an explicit and sharp bound on the mixing time that scales polynomially with system size. For more general spin systems, we develop a tensor network-based algorithm for constructing the Lindblad jump operator and for simulating the dynamics. Using this algorithm, we demonstrate numerically that dissipative ground state preparation protocols can achieve...

Predicting Open Quantum Dynamics with Data-Informed Quantum-Classical Dynamics

Wed, 25 Mar 2026 00:00:00 +0000

We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equation with a flexible, time-dependent Hamiltonian that can be optimized to fit sparse and noisy data from local observations of an extensive open quantum system. We demonstrate the accuracy and efficiency of DIQCD for both experimental and simulated quantum devices. We show that DIQCD can predict entanglement dynamics of ultracold molecules (calcium fluoride) in optical tweezer arrays. DIQCD also successfully predicts carrier mobility in organic semiconductors (rubrene) with accuracy comparable to nearly exact numerical methods.

Efficient Decision Trees for Tensor Regressions

Tue, 24 Mar 2026 00:00:00 +0000

We proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output regression tree models whose input variables are tensors (i.e., multi-way arrays). We devised and implemented fast randomized and deterministic algorithms for efficient fitting of scalar-on-tensor trees, making TT competitive against tensor-input GP models (Sun et al., 2023; Yu et al., 2018). Based on scalar-on-tensor tree models, we extend our method to tensor-on-tensor problems using additive tree ensemble approaches. Theoretical justification and extensive experiments, including testing robustness to entrywise input tensor noise, are provided on real and synthetic datasets to illustrate the performance of TT. Our implementation is provided at http://www.github.com/hrluo.

Numerically exact configuration interaction at quadrillion-determinant scale

Tue, 24 Mar 2026 00:00:00 +0000

The combinatorial growth of configuration interaction (CI) has long limited this formally exact quantum chemistry method to only the smallest molecules. Here, we report a numerically exact CI calculation exceeding one quadrillion (1015) determinants, made possible by a lossless categorical compression strategy within the small-tensor-product distributed active space (STP-DAS) framework. This approach overcomes the traditional memory bottlenecks of CI by a numerically exact compression of the wavefunction representation and reformulating the most computationally demanding matrix–vector operations. Using this method, we performed a fully relativistic CI calculation of the ground state of HBrTe with over 1015 complex-valued determinants in just 34.5 h on 1000 computing nodes—the largest CI calculation ever reported. We further achieved fast computation for systems with hundreds of billions of determinants on only a few compute nodes. Extensive benchmarks confirm that the method retains...

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Mon, 23 Mar 2026 00:00:00 +0000

Physically-consistent quantification of flood risks in global models requires kilometer scale flood simulations using 2D physics schemes, both of which are unavailable in the current generation Earth System Models. In this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation library for the Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries to run efficiently on CPUs and GPUs. RDycore was validated for analytical, manufactured, and a well-studied dam break problem. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs on Perlmutter and Frontier supercomputers, respectively. The one-way E3SM-RDycore coupling is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The work presented here is the foundational step in providing hardware and algorithmic portability...

Models and Algorithms for Equilibrium Analysis of Mixed-Material Nucleic Acid Systems

Thu, 19 Mar 2026 00:00:00 +0000

Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2'OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides,...

A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-Time Dyson Expansion

Fri, 13 Mar 2026 00:00:00 +0000

Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on subfemtosecond time scales. The interpretation of experimental results is often enhanced by parallel numerical studies, which can provide insight and validation for experimental hypotheses. However, developing a theoretical framework for simulating time-resolved spectra remains a significant challenge. The most suitable approach involves the many-body nonequilibrium Green's function formalism, which accounts for crucial dynamical many-body correlations during time evolution. While these dynamical correlations are essential for observing emergent behavior in time-resolved spectra, they also render the formalism prohibitively expensive for large-scale simulations. Substantial effort has been devoted to reducing this computational cost─through approximations and numerical techniques─while preserving the key dynamical correlations. The ultimate goal...

Diagonal state designs with reconfigurable real-time circuits

Wed, 11 Mar 2026 00:00:00 +0000

Unitary designs are widely used in quantum computation, but in many practical settings it suffices to construct a diagonal state design generated with unitary gates diagonal in the computational basis. In this work, we introduce a simple and efficient diagonal state 3-design based on real-time evolutions under 2-local Hamiltonians. Our construction is inspired by the classical Girard-Hutchinson trace estimator in that it involves the stochastic preparation of many random-phase states. Though the exact Girard-Hutchinson states are not tractably implementable on a quantum computer, we can construct states that match the statistical moments of the Girard-Hutchinson states with real-time evolution. Importantly, our random states are all generated using the same Hamiltonians for real-time evolution, with the randomness arising solely from stochastic variations in the durations of the evolutions. In this sense, the circuit is fully reconfigurable and thus suited for near-term realizations...

Activation of methane by U+ studied by guided ion beam tandem mass spectrometry and quantum chemistry

Wed, 11 Mar 2026 00:00:00 +0000

Reaction pathways of all products formed in the U+ + CH4 (CD4) reaction were explored as a function of kinetic energy using guided ion beam tandem mass spectrometry and quantum chemical calculations. UH+, UC+, UCH+, UCH2+, and UCH3+ (and their perdeuterated analogues) are formed in endothermic reactions. In both systems, the UCH2+ (UCD2+) dehydrogenated product was the dominant product in the low-energy region, whereas the UH+ (UD+) hydride product became predominant at high energies. The kinetic energy behavior of the various products is consistent with a common intermediate of H-U+-CH3 (D-U+-CD3). The kinetic energy dependence of all product cross sections was modeled to obtain experimental bond dissociation energies at 0 K (in eV): D0 (U+-H) = 2.42 ± 0.10, D0 (U+-C) = 3.95 ± 0.12, D0 (U+-CH) = 4.91 ± 0.09, D0 (U+-CH2) = 4.11 ± 0.04, and D0 (U+-CH3) = 2.41 ± 0.09. Quantum chemical calculations using the UCCSD(T) and UB3LYP approaches with the cc-pwCVXZ-PP basis set with...

A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation

Wed, 11 Mar 2026 00:00:00 +0000

Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However, current quantum algorithms for Hamiltonian simulation yield results that are inadequate for real use cases and suffer from lengthy execution times when implemented on near-term quantum hardware. In this work, we introduce a framework for evaluating the performance of quantum simulation algorithms, focusing on the computation of observables, such as energy expectation values. Our framework provides end-to-end demonstrations of algorithmic optimizations that utilize Pauli term groups based on $k$-commutativity, generate customized Clifford measurement circuits, and implement weighted shot distribution strategies across these groups. These demonstrations span multiple quantum execution environments, allowing us to identify critical factors influencing runtime and solution accuracy. We integrate enhancements...

Simulating Hurricane Katrina in the Simple Cloud‐Resolving E3SM Atmosphere Model v1

Fri, 6 Mar 2026 00:00:00 +0000

Abstract Climate models are important tools for advancing understanding and prediction of tropical cyclones (TCs). Traditional global climate models, however, do not have the ability to properly simulate TC intensity due to their coarse horizontal resolution. Regional models can be run at convection‐permitting resolutions, but these models are often strongly influenced by the data used in the lateral boundary forcing, and domain choice can have a large impact on the simulation. Cloud‐resolving global climate models have demonstrated great potential for realism in TC simulations, and in this study we focus specifically on the Simple Cloud‐Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) v1 configuration. We evaluate SCREAMv1 against the observational record and the Weather Research and Forecasting (WRF) model run at a convection‐permitting resolution with Hurricane Katrina as our case study. We found that both models produced realistic simulations of...

StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction

Fri, 27 Feb 2026 00:00:00 +0000

Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The complexity arises from the intricate interactions between scales and the interplay of diverse physical processes, which manifest in PDEs through coupled, nonlinear terms that govern the evolution of multiple physical fields across scales. Neural operators have shown potential in short-term prediction of such complex spatio-temporal dynamics; however, achieving stable high-fidelity predictions and providing robust uncertainty quantification over extended time horizons remains an open and unsolved area of research. These limitations often lead to stability degradation with rapid error accumulation, particularly in long-term forecasting of systems characterized by multi-scale behaviors involving dynamics of different orders. To address these challenges, we propose an autoregressive Spatio-temporal Fourier Transformer...

StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction

Fri, 27 Feb 2026 00:00:00 +0000

US11 - Edits for intrinsics for source location

Thu, 26 Feb 2026 00:00:00 +0000

This paper contains Fortran 202Y specification edits for Fortran 202Y work item US11: Intrinsics for source location. It passed by unanimous consent at the Feb 2026 meeting #239 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Toward accelerating rare-earth metal extraction using equivariant neural networks

Tue, 24 Feb 2026 00:00:00 +0000

A high-throughput workflow leveraging equivariant GNNs and a diverse dataset of rare-earth complexes to predict binding affinities and accelerate critical metal separation. The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their...

StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction

Tue, 24 Feb 2026 00:00:00 +0000

StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction

Mon, 23 Feb 2026 00:00:00 +0000

ZTF SN Ia DR2 follow-up: Exploring the origin of the Type Ia supernova host galaxy step through Si II velocities

Tue, 17 Feb 2026 00:00:00 +0000

The relation between Type Ia supernovae (SNe Ia) and the stellar masses of their host galaxy is well documented. In particular, Hubble residuals display a distinct luminosity shift based on host mass. This is known as the mass step. This effect is widely used as an additional correction factor in the standardisation of SN Ia luminosities. We investigate the Hubble residuals and the mass step of normal SNe Ia in the context of Si  II λ 6355 velocities based on 277 normal SNe Ia that are near their peak in the second data release (DR2) of the Zwicky Transient Facility (ZTF). We divided the sample into high-velocity (HV) and normal-velocity (NV) SNe Ia, separated at 12,000 km s −1 . This produced a sample of 70 HV and 207 NV objects. We then explored potential environment- and/or progenitor-related effects by investigating the Si  II λ 6355 velocities with parameters such as the light-curve stretch x 1 , the colour c , and the host galaxy properties. Although we only find a marginal...

Direct interpolative construction of the discrete Fourier transform as a matrix product operator

Wed, 11 Feb 2026 00:00:00 +0000

The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator [1]. However, the original proof of this fact does not furnish a construction of the MPO with a guaranteed error bound. Meanwhile, the existing practical construction of this MPO, based on the compression of a quantum circuit, is not as efficient as possible. We present a simple closed-form construction of the QFT MPO using the interpolative decomposition, with guaranteed near-optimal compression error for a given rank. This construction can speed up the application of the QFT and the DFT, respectively, in quantum circuit simulations and QTT applications. We also connect our interpolative construction to the approximate quantum Fourier transform (AQFT) by demonstrating that the AQFT can be viewed as an MPO constructed using a different interpolation scheme.

Benchmark-driven Models for Energy Analysis and Attribution of GPU-Accelerated Supercomputing

Tue, 3 Feb 2026 00:00:00 +0000

As advances in energy-efficiency become the primary limiter to increases in power-constrained supercomputing and machine learning performance, it is imperative developers, architects, and practitioners understand how modern GPUs consume energy when running HPC and ML applications. Rather than opaque coarse-grained metrics, in this paper, we develop an extensible, microbenchmark-parameterized energy model capable of attributing application energy not only by functional unit (FPU, tensor core, integer ALU) and memory level (L1, L2, HBM), but can also differentiate control energy from datapath energy. We examine trends in energy per operation among four generations of GPUs and validate our results using supercomputing and ML/AI procurement workloads. Our insights and extrapolations can be used to drive the future of CMOS and memory technologies, computer architecture research, algorithmic innovation, optimizations for power-constrained and mobile environments, and data center operations.

Roofline Analysis of Tightly-Coupled CPU-GPU Superchips: A Study on MI300A and GH200

Tue, 3 Feb 2026 00:00:00 +0000

The introduction of tightly-coupled heterogeneous architectures, such as AMD’s MI300A and NVIDIA’s Grace-Hopper (GH200), address a long-standing bottleneck in accelerated computing, namely the CPU-GPU interface. Whereas the GH200 can be seen as a technological leap in CPU-GPU connectivity greatly exceeding PCIe cadence, the unified memory architecture of the MI300A APU enables seamless communication through coherent caches. When the CPU and GPU execute concurrently, they contend not only for finite bandwidth on their shared memory interfaces, but they also contend for power in a power-constrained environment. Whereas the serialized producer-consumer, overlapped/pipelined producer-consumer, and multi-tenancy (i.e., shared virtualized hardware) execution models offer clear avenues for exploiting these architectures to increase HPC and ML/AI throughput, they come with clear risks associated with non-linear performance degradation arising from this contention. In this paper, we extend...

Assessing the performance of solar radiation management geoengineering simulations

Thu, 22 Jan 2026 00:00:00 +0000

Offsetting the global warming caused by anthropogenic increases in atmospheric greenhouse gases by deliberate injection of aerosols into the stratosphere is the most studied of solar radiation management geoengineering schemes. The long-term success or failure of such schemes in achieving their stated goals is assessed by comparing simulated geoengineered temperature, precipitation and tropical cyclones metrics to equivalent fields in the simulated targeted climate simulations. Results using available data sets from three single model stabilized climate target experiments and three multimodel climate change reduction experiments are presented and compared against a measure of internal variability. While all but one experimental scheme is successful in achieving their targeted global mean annual surface temperature, their success at regional scales varies significantly and is often larger than the internal variability metric used here.

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

Thu, 22 Jan 2026 00:00:00 +0000

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the...

Please, No More Loops (Than Necessary): New Patterns in Fortran 2023

Thu, 22 Jan 2026 00:00:00 +0000

Loops are seemingly ubiquitous in programming and yet writing loops provides one example of a common practice stuck in a pattern as old as high-level programming languages themselves. This webinar will provide an overview of the features introduced in Fortran standards from Fortran 90 to 2023. We will venture into often-unvisited nooks and crannies and traverse equally unvisited expansive pastures. Weaving feature groups together by the approaches they enable, the talk will emphasize array, object-oriented, parallel, modular, and functional programming patterns and paradigms. The talk will demonstrate the utility of the described features in open-source packages developed by Berkeley Lab’s Computer Languages and System Software (CLaSS) Group and our collaborators. The presentation will emphasize expressiveness and conciseness, showing how our Julienne correctness-checking framework supports writing assertions and unit tests using natural-language idioms; how we write textbook-form...

Molecular fluctuations inhibit intermittency in compressible turbulence

Wed, 21 Jan 2026 00:00:00 +0000

In the standard picture of fully developed turbulence, highly intermittent hydrodynamic fields are nonlinearly coupled across scales, where local energy cascades from large scales into dissipative vortices and large density gradients. Microscopically, however, constituent fluid molecules are in constant thermal (Brownian) motion, but the role of molecular fluctuations in large-scale turbulence is largely unknown, and with rare exceptions, it has historically been considered irrelevant at scales larger than the molecular mean free path. Recent theoretical and computational investigations have shown that molecular fluctuations can impact energy cascade at Kolmogorov length scales. Here, we show that molecular fluctuations not only modify energy spectrum at wavelengths larger than the Kolmogorov length in compressible turbulence, but also significantly inhibit spatio-temporal intermittency across the entire dissipation range. Using large-scale direct numerical simulations of computational...

Photon blockade in a Tavis-Cummings system

Wed, 21 Jan 2026 00:00:00 +0000

We observe blockade of microwave photons in a Tavis-Cummings system comprising a superconducting cavity and up to transmon qubits. The effect is characterized with photon-number-resolving spectroscopy using an additional dispersively coupled transmon “witness” qubit to directly probe the cavity’s photon-number distribution. We first observe polariton formation with splitting proportional to , confirming the Tavis-Cummings coupling, and subsequently obtain sub-Poissonian cavity photon statistics when the cavity is driven at polariton frequencies.

US20: Edits for Local Prefix Operation Intrinsics

Fri, 16 Jan 2026 00:00:00 +0000

This paper contains Fortran 202Y specification edits for Fortran 202Y work item US20: Local Prefix Operation Intrinsics. It passed by unanimous consent at the Jan 2026 meeting #238 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Open2C

Wed, 14 Jan 2026 00:00:00 +0000

A cache-coherent memory subsystem plays an important role in complex digital computing systems. It maintains memory consistency across on-chip caches that hide the memory latency to improve computational performance. Being managed by hardware, the cache subsystem facilitates multi-core system programming and allows developers to focus on other crucial aspects. However, due to extensive protocol-related traffic and lack of explicit data movement management, cache memory scalability becomes a big concern. Existing evaluation techniques, such as cycle-approximate estimation or cycle-accurate simulation, do not guarantee accurate and fast results in the first case or require tremendous amount of efforts to implement and modify the system in the second. We present Open Cache Coherence (Open2C). The project aims to provide a powerful yet flexible and easy-to-extend tool that enables exploring coherent cache memory subsystem for upcoming large-scale computing systems. Open2C includes...

Compactly‐Supported Nonstationary Kernels for Computing Exact Gaussian Processes on Big Data

Wed, 14 Jan 2026 00:00:00 +0000

ABSTRACT The Gaussian process (GP) is a widely used method for analyzing large‐scale data sets, including spatio‐temporal measurements of nonlinear processes that are now commonplace in the environmental sciences. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about 10,000 points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high‐performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable...

Bridging Simulation and Silicon: A Study of RISC-V Hardware and FireSim Simulation

Wed, 14 Jan 2026 00:00:00 +0000

RISC-V ISA-based processors have recently emerged as both powerful and energy-efficient computing platforms. The release of the MILK-V Pioneer marked a significant milestone as the first desktop-grade RISC-V system. With increasing engagement from both academia and industry, such platforms exhibit strong potential for adoption in high-performance computing (HPC) environments. The open-source, FPGA-accelerated FireSim framework emerged as a flexible and scalable tool for architectural exploration, enabling simulation of various system configurations using RISC-V cores. Despite its capabilities, there remains a lack of systematic evaluation regarding the feasibility and performance prediction accuracy of FireSim when compared to physical hardware. In this study, we address this gap by modeling a commercially available single-board computer and a desktop-grade RISC-V CPU within FireSim. To ensure fidelity between simulation and real hardware, we first measure the performance of a...

xBGAS

Wed, 14 Jan 2026 00:00:00 +0000

Given the switch from monolithic architectures to integrated systems of commodity components, scalable high performance computing architectures often suffer from unwanted latencies when operations depart an individual device domain. Transferring control and/or data across loosely coupled commodity devices implies a certain degree of cooperating in the form of complex system software. The end result being a total system architecture the operates in an inefficient manner. This work presents initial research into creating micro architecture extensions to the RISC-V instruction set that provide tightly coupled support for common high performance computing operations. This xBGAS micro architecture extension provides applications the ability to access globally shared memory blocks directly from rudimentary instructions. The end result being a highly efficient micro architecture for scalable shared memory programming environments.

Exascale Computational Fluid Dynamics in Heterogeneous Systems

Tue, 13 Jan 2026 00:00:00 +0000

Abstract Exascale computing has extended the reach of resolved flow simulations in complex, heterogeneous systems far beyond conventional computational fluid dynamics capabilities. As a result, unprecedented pore and microscale resolution have been achieved in domains that have been traditionally modeled by, and limited to, continuum, effective medium approaches. By making use of computational resources on the new exascale supercomputer, Frontier, at the Oak Ridge Leadership Computing Facility, we performed flow simulations that have pushed the limits of domain-to-resolution ratios by several orders of magnitude for heterogeneous media. Our approach is an incompressible, Navier–Stokes CFD solver based on adaptive, embedded boundary (EB) methods supported by the Chombo software framework for applied partial differential equations (PDEs). The computational workhorse in the CFD application code is an elliptic solver framework in Chombo for pressure-Poisson and viscous, Helmholtz...

Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites

Tue, 13 Jan 2026 00:00:00 +0000

A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle-reinforced metal matrix nanocomposites (MMNCs). Using aluminum–silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification-regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning-enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by...

SynthEsizing Novel H2 Sensors for Operational Resilience in Pipeline Infrastructure (SENSOR)

Tue, 13 Jan 2026 00:00:00 +0000

A flexible and extensible computational framework acts as a black-box materials discovery engine, capable of screening, predicting, and designing advanced materials with minimal manual intervention was developed. While developed for hydrogen sensing, the approach can be readily adapted to other materials challenges, offering a powerful tool for data-driven materials innovation.

Quantum Algorithm for Linear Non-unitary Dynamics with Near-Optimal Dependence on All Parameters

Mon, 12 Jan 2026 00:00:00 +0000

We introduce a family of identities that express general linear non-unitary evolution operators as a linear combination of unitary evolution operators, each solving a Hamiltonian simulation problem. This formulation can exponentially enhance the accuracy of the recently introduced linear combination of Hamiltonian simulation (LCHS) method [An, Liu, and Lin, Physical Review Letters, 2023]. For the first time, this approach enables quantum algorithms to solve linear differential equations with both optimal state preparation cost and near-optimal scaling in matrix queries on all parameters.

Intrinsic structure of relaxor ferroelectrics from first principles

Sat, 10 Jan 2026 00:00:00 +0000

We develop FIRE-Swap, a first-principles framework for sampling intrinsic compositional structures in complex perovskites with machine-learning interatomic potentials (MLIPs). Using both dedicated and universal MLIPs, we study the relaxor lead magnesium niobate (PMN) and the solid solutions lead zirconate titanate (PZT) and lead strontium titanate (PST). Across MLIP models and exchange-correlation approximations, FIRE-Swap robustly predicts a rock-salt-like chemical order in PMN, which is absent in PZT and PST with the same mixing ratio, consistent with experiments. We further identify in PMN a distinct Nb-cluster morphology. Interconnected, non-coarsened polar nanoregions are found within Nb clusters, providing a mesoscale basis for understanding relaxor ferroelectricity.

Implementation of a Mesh refinement algorithm into the quasi-static PIC code QuickPIC

Thu, 8 Jan 2026 00:00:00 +0000

Plasma-based acceleration (PBA) has emerged as a promising candidate for the accelerator technology used to build a future linear collider and/or an advanced light source. In PBA, a trailing or witness particle beam is accelerated in the plasma wave wakefield (WF) created by a laser or particle beam driver. The WF is often nonlinear and involves the crossing of plasma particle trajectories in real space and thus particle-in-cell methods are used. The distance over which the drive beam evolves is several orders of magnitude larger than the wake wavelength. This large disparity in length scales is amenable to the quasi-static approach. Three-dimensional (3D), quasi-static (QS), particle-in-cell (PIC) codes, e.g., QuickPIC, have been shown to provide high fidelity simulation capability with 2-4 orders of magnitude speedup over 3D fully explicit PIC codes. In PBA, the witness beam needs to be matched to the focusing forces of the WF to reduce the emittance growth. In some linear collider...

ZTF SN Ia DR2: Cosmology-independent constraints on Type Ia supernova standardisation from supernova siblings

Mon, 5 Jan 2026 00:00:00 +0000

Understanding Type Ia supernovae (SNe Ia) and the empirical standardisation relations that make them excellent distance indicators is vital to improving cosmological constraints. SN Ia ‘siblings, i.e. two or more SNe Ia in the same host or parent galaxy, offer a unique way to infer the standardisation relations and their scatter across the population. We analysed a sample of 25 SN Ia pairs observed homogeneously by the Zwicky Transient Facility (ZTF) to infer the SNe Ia light curve width-luminosity and colour-luminosity parameters, α and β . Using the pairwise constraints from siblings, which allow for a scatter in the standardisation relations, we found α = 0.218 ± 0.055 and β = 3.084 ± 0.312, respectively, with a dispersion in α and β of ≤0.195 and ≤0.923, respectively, at a 95% confidence level. While the median dispersion is large, the values within ∼1 σ are consistent with no dispersion. Hence, fitting for a single global standardisation relation, we found α = 0.228 ± 0.029...

FAIR Universe 2024: Higgs ML Uncertainty Challenge

Mon, 5 Jan 2026 00:00:00 +0000

The HiggsML Uncertainty Challenge is a machine learning competition aimed at improving uncertainty-aware AI techniques in high-energy physics. Part of the FAIR Universe initiative, focuses on estimating the Higgs boson signal strength while accounting for systematic uncertainties affecting collider experiments. Unlike traditional classification tasks, participants must construct confidence intervals that properly cover systematic distortions. The HiggsML Uncertainty Challenge establishes a benchmark for uncertainty-aware AI, with applications in high-energy physics and beyond. The competition is hosted on Codabench, an open AI benchmarking platform, and uses highperformance computing resources at NERSC Perlmutter for scalable and reproducible model evaluation. The dataset and evaluation framework will remain publicly available for continued research.

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

Sat, 3 Jan 2026 00:00:00 +0000

Generic Hilbert space fragmentation in Kogut-Susskind lattice gauge theories

Fri, 19 Dec 2025 00:00:00 +0000

At the heart of quantum many-body physics lies the understanding of mechanisms that avoid quantum thermalization in an isolated system quenched far from equilibrium. A prominent example is Hilbert space fragmentation, which has recently emerged as an ergodicity-breaking mechanism in constrained spin models. Here, we show that Kogut-Susskind formulations of lattice gauge theories in d+1D (d spatial and one temporal dimensions) give rise to Hilbert space fragmentation, and discuss possible implications for understanding continuum physics. Our findings not only prove that lattice gauge theories are a natural platform for Hilbert space fragmentation, they also serve as a guide to the conditions under which these models can be faithfully used to infer the thermalization properties of quantum chromodynamics.

Anti-symmetric barron functions and their approximation with sums of determinants

Wed, 17 Dec 2025 00:00:00 +0000

A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles. The architecture of neural network models for the electron wave function typically comprises an equivariant component followed by a summation of determinants. The recently introduced Generic Antisymmetric (GA) block is designed to enhance the expressivity of such neural wave functions, and it was found that the 2-layer GA block achieved more accurate energies than the corresponding single-determinant FermiNet architecure, suggesting its promise as a way to improve the expressivity of neural wave functions. In this paper we show how the function expressed by the 2-layer GA block can be decomposed into a sum of determinants. We formalize this result by defining the antisymmetric Barron space as a generalized version of the 2-layer GA block and providing an appromation theorem for this function class. This result can be viewed as a negative...

Dissipative ground state preparation in ab initio electronic structure theory

Wed, 17 Dec 2025 00:00:00 +0000

Dissipative engineering is a powerful tool for quantum state preparation, and has drawn significant attention in quantum algorithms and quantum many-body physics in recent years. In this work, we introduce a novel approach using the Lindblad dynamics to efficiently prepare the ground state for general ab initio electronic structure problems on quantum computers, without variational parameters. These problems often involve Hamiltonians that lack geometric locality or sparsity structures, which we address by proposing two generic types of jump operators for the Lindblad dynamics. Type-I jump operators break the particle number symmetry and should be simulated in the Fock space. Type-II jump operators preserves the particle number symmetry and can be simulated more efficiently in the full configuration interaction space. For both types of jump operators, we prove that in a simplified Hartree-Fock framework, the spectral gap of our Lindbladian is lower bounded by a universal constant....

Digital quantum simulation of cavity quantum electrodynamics: insights from superconducting and trapped ion quantum testbeds

Tue, 16 Dec 2025 00:00:00 +0000

We explore the potential for hybrid development of quantum hardware where currently available quantum computers simulate open cavity quantum electrodynamical (CQED) systems for applications in optical quantum communication, simulation and computing. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis–Cummings model containing N atoms coupled to a lossy cavity. We report the results of executing this algorithm on two noisy intermediate-scale quantum computers: a superconducting processor and a trapped ion processor, to simulate the population dynamics of an open CQED system featuring N = 3 atoms. By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform, obtaining results which agree closely with the exact solution of the system. These results can be used as a recipe for efficient and platform-specific quantum simulation...

Parallelizing autotuning for HPC applications: Unveiling the potential of the speculation strategy in Bayesian optimization

Tue, 16 Dec 2025 00:00:00 +0000

In the exascale computing era, tuning High-Performance Computing (HPC) applications has become a significant computational challenge. Although Bayesian optimization (BO) has emerged as a promising tool for HPC performance tuning, the BO workflow is inherently sequential (i.e., one function evaluation at a time) and cannot leverage the huge amount of parallel resources present in modern supercomputers, resulting in a considerable underutilization of their computational capabilities. This paper explores the trade-off between search quality and parallelism in BO, investigating a diverse set of methods. Building upon both previous approaches from the literature and novel methodologies introduced in this work, our study provides a deep analysis to accelerate BO performance tuning. By examining a set of synthetic functions and practical HPC applications, our exploration analyzes the interaction among various BO methods for parallelization, the quantity of parallel resources, the runtime...

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Mon, 15 Dec 2025 00:00:00 +0000

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software....

Quantum optimal control of superconducting qubits based on machine-learning characterization

Thu, 11 Dec 2025 00:00:00 +0000

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. We demonstrate that this framework produces an accurate description of the device...