Computing Sciences

Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules, such as small molecules and nucleic acids. Such advancements are accelerating biomedical research and the development of new protein design and bioengineering methods among many other important biotechnology applications. In this review, we outline the recent advances in protein-centric biomolecular structure and interaction prediction, highlight some major challenges in the field, and discuss potential directions to address them.

Due to an error in production, the error margins of many values were incorrectly published. Table 1 details the locations of the errors and their corrections.

We study finite-volume (FV) corrections to determinations of via lattice quantum chromodynamics (QCD) using analytic results and numerical analysis. We observe that heavy Baryon chiral perturbation theory does not provide an unambiguous prediction for the sign of the FV correction, which is not surprising when one also considers large- constraints on the axial couplings. We further show that nonmonotonic FV corrections are naturally allowed when one considers either including explicit -resonance degrees of freedom or one works to higher orders in the chiral expansion. We investigate the potential impact of these FV corrections with a precision study of using models of FV corrections that are monotonic and nonmonotonic. Using lattice QCD data that is approximately at the 1% level of precision, we do not see significant evidence of nonmonotonic corrections. Looking forward to the next phase of lattice QCD calculations, we estimate that calculations that are between the 0.1% and 1% level of precision may be sensitive to these FV artifacts. Finally, we present an update of the CalLat prediction of in the isospin limit with subpercent precision, .

Parton distribution functions (PDFs) are central to precision QCD phenomenology. Their Mellin moments can be computed on the lattice, but direct determinations using local operators, besides ⟨x⟩, face severe challenges from reduced hypercubic symmetry, limiting results to the lowest moments. A recently proposed method resolves these issues using gradient flow. We demonstrate the efficacy of this method by computing ratios of flavor nonsinglet pion PDF moments up to ⟨x^{5}⟩ on four lattice spacings at m_{π}≃411  MeV. The moments and reconstructed PDF agree quantitatively with recent phenomenological extractions.

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. We summarize the DR1 observations, the spectroscopic data-reduction pipeline and data products, large-scale structure catalogs, value-added catalogs, and describe how to access and interact with the data. In addition to fulfilling its core cosmological objectives with unprecedented precision, we expect DR1 to enable a wide range of transformational astrophysical studies and discoveries.

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 and operating power-efficient, sustainable supercomputing systems.

StochasticGW is a code for computing accurate quasi-particle (QP) energies of molecules and material systems in the GW approximation. StochasticGW utilizes the stochastic Resolution of the Identity (sROI) technique to enable a massively parallel implementation with computational costs that scale semilinearly with system size, allowing the method to access systems with tens of thousands of electrons. We introduce a new implementation, StochasticGW-GPU, for which the main bottleneck steps have been ported to GPUs and give substantial performance improvements over previous versions of the code. We showcase the new code by computing band gaps of hydrogenated silicon clusters (Si_xH_y) containing up to 10,001 atoms and 35,144 electrons, and we obtain individual QP energies with a statistical precision of better than ±0.03 eV with times-to-solution of less than 1 h.

Numerous problems in bioinformatics and computational biology can be framed as a task of learning a mapping from one state of a biological system to another relevant state or of exploring novel data points across biologically constrained spaces. However, manually deriving such mappings—for example, to transform cells in a diseased state back into a healthy state, or extrapolating from existing datasets to create new data—is often non-trivial and can require extraordinary domain expertise and resources. Fortunately, the field of generative artificial intelligence (AI) has introduced a new training paradigm referred to as (conditional) flow matching, which has emerged as a promising solution to this problem, with broad applicability in computer vision, natural language processing, and the physical and life sciences. Flow matching is a powerful and principled, data-driven framework for efficiently learning a mapping between arbitrary pairs of high-dimensional data distributions, making it well suited for addressing problems in molecular and cell biology. In this Review, we characterize the theoretical foundations of flow matching and its applications in biomolecular modelling for small molecules, proteins, DNA/RNA, and their interactions, as well as its uses in single/multi-cellular modelling for cell phenotyping and imaging, each contributing towards the development of an AI-based virtual cell. Finally, this review highlights open-source flow-matching methods and discusses future directions in flow-based generative modelling for bioinformatics and computational biology.

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 interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

We compute the Euclidean hadronic tensor from charge density operators and extract elastic and resonance structures by employing exponential fits to the four-point function correlator, as well as a Bayesian reconstruction inverse algorithm to obtain the corresponding spectral density for qualitative comparison. We present the determination of the nucleon’s Sachs electric form factor using the hadronic tensor formalism and verify that it is consistent with that from the conventional three-point function calculation. Beyond the elastic peak, we observe a structure located approximately 0.5–0.7 GeV above the nucleon mass in the Bayesian reconstruction. This structure is interpreted as a mixture of the Roper resonance [ N ( 1440 ) ], and states with both positive and negative parities in this mass region, as well as multihadron states. Assuming the observed structure is dominated by J P = 1 / 2 ± states, we extract the transition electric form factor G E * ( Q 2 ) and the corresponding longitudinal helicity amplitude S 1 / 2 ( Q 2 ) , and compare them with those determined from the CLAS experimental data of nucleon-to-Roper transition. Although fitting to the four-point correlation function or using the inverse algorithm does not resolve individual resonances, it nevertheless enables the determination of total inclusive lepton–nucleon scattering cross sections in appropriate energy bins. This lattice QCD calculation presents the first major step toward studying the inclusive N → X contributions within the hadronic tensor formalism.