Bourns College of Engineering

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summary: Program Title: VAN-DAMME CPC Library link to program files:: https://doi.org/10.17632/zcgw2n5bjf.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Nature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Thermal treatment of perfluoroalkyl and polyfluoroalkyl substances (PFASs) presents a promising opportunity to halt the PFAS cycle. However, how co-occurring materials such as granular activated carbon (GAC) influence thermal decomposition products of PFASs, and underlying mechanisms remain unclear. We studied the pyrolysis of two potassium salts of perfluoroalkanesulfonates (PFSAs, C_nF_2n+1SO₃K), perfluorobutanesulfonate (PFBS-K), and perfluorooctanesulfonate (PFOS-K), with or without GAC. PFBS-K is more stable than PFOS-K for pure standards, but when it is adsorbed onto GAC, its thermal stabilities and decomposition behaviors are similar. Temperatures and heating rates can significantly influence the decomposition mechanisms and products for pure standards, while these effects are less pronounced when PFSAs are adsorbed onto GAC. We further studied the underlying decomposition mechanisms. Pure standards of C_nF_2n+1SO₃K can decompose directly in their condense phase by reactions: F(CF₂)_nSO₃K → F(CF₂)_n-2CF═CF₂ + KFSO₃ or F(CF₂)_nSO₃K → F(CF₂)_n^- + K⁺ + SO₃. GAC appears to facilitate breakage of the C-S bond to release SO₂ at temperatures as low as 280 °C. GAC promotes fluorine mineralization through functional reactive sites. SiO₂ is particularly important for the surface-mediated mineralization of PFASs into SiF₄. These findings offer valuable insights into optimizing thermal treatment strategies for PFAS-contaminated waste.

We present a new software module, QRCODE (Quantum Research for Calculating Optically Driven Excitations), for massively parallelized real-time time-dependent density functional theory (RT-TDDFT) calculations of periodic systems in the open-source Qbox software package. Our approach utilizes a custom implementation of a fast Fourier transformation scheme that significantly reduces inter-node message passing interface (MPI) communication of the major computational kernel and shows impressive scaling up to 16,344 CPU cores. In addition to improving computational performance, QRCODE contains a suite of various time propagators for accurate RT-TDDFT calculations. As benchmark applications of QRCODE, we calculate the current density and optical absorption spectra of hexagonal boron nitride (h-BN) and photo-driven reaction dynamics of the ozone-oxygen reaction. We also calculate the second and higher harmonic generation of monolayer and multi-layer boron nitride structures as examples of large material systems. Our optimized implementation of RT-TDDFT in QRCODE enables large-scale calculations of real-time electron dynamics of chemical and material systems with enhanced computational performance and impressive scaling across several thousand CPU cores.

CRISPR-Cas systems have transformed the field of synthetic biology by providing a versatile method for genome editing. The efficiency of CRISPR systems is largely dependent on the sequence of the constituent sgRNA, necessitating the development of computational methods for designing active sgRNAs. While deep learning-based models have shown promise in predicting sgRNA activity, the accuracy of prediction is primarily governed by the data set used in model training. Here, we trained a convolutional neural network (CNN) model and a large language model (LLM) on balanced and imbalanced data sets generated from CRISPR-Cas12a screening data for the yeast Yarrowia lipolytica and evaluated their ability to predict high- and low-activity sgRNAs. We further tested whether prediction performance can be improved by training on imbalanced data sets augmented with synthetic sgRNAs. Lastly, we demonstrated that adding synthetic sgRNAs to inherently imbalanced CRISPR-Cas9 data sets from Y. lipolytica and Komagataella phaffii leads to improved performance in predicting sgRNA activity, thus underscoring the importance of employing balanced training sets for accurate sgRNA activity prediction.

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens.

Plant natural products (PNPs) hold significant pharmaceutical importance. The sessile nature of plants has led to the evolution of chemical defense mechanisms over millions of years to combat environmental challenges, making it a crucial and essential defense weapon. Despite their importance, the abundance of these bioactive molecules in plants is typically low, and conventional methods are time-consuming for enhancing production. Moreover, there is a pressing need for novel drug leads, exemplified by the shortage of antibiotics and anticancer drugs. Understanding how plants respond to stress and regulate metabolism to produce these molecules presents an opportunity to explore new avenues for discovering compounds that are typically under the detection limit or not naturally produced. Additionally, this knowledge can contribute to the advancement of plant engineering, enabling the development of new chassis for the biomanufacturing of these valuable molecules. In this perspective, we explore the intricate regulation of PNP biosynthesis in plants, and discuss the biotechnology strategies that have been and can be utilized for the discovery and production enhancement of PNPs in plants.

We present an open-source software package, TRAVOLTA (Terrific Refinements to Accelerate, Validate, and Optimize Large Time-dependent Algorithms), for carrying out massively parallelized quantum optimal control calculations on GPUs. The TRAVOLTA software package is a significant overhaul of our previous NIC-CAGE algorithm and also includes algorithmic improvements to the gradient ascent procedure to enable faster convergence. We examine three different variants of GPU parallelization to assess their performance in constructing optimal control fields in a variety of quantum systems. In addition, we provide several examples with extensive benchmarks of our GPU-enhanced TRAVOLTA code to show that it generates the same results as previous CPU-based algorithms but with a speedup that is more than ten times faster. Our GPU enhancements and algorithmic improvements enable large quantum optimal control calculations that can be efficiently and routinely executed on modern multi-core computational hardware. Program summary: Program Title: TRAVOLTA CPC Library link to program files: https://doi.org/10.17632/grwppm37rn.1 Licensing provisions: GNU General Public License 3 Programming language: C++, openBLAS, and CUDA Supplementary material: Brief review of LU decomposition, raw numerical values used to generate Fig. 6 in the main text, and input examples for the TRAVOLTA software package. Nature of problem: The TRAVOLTA software package utilizes GPU accelerated routines and new algorithmic improvements to compute optimized electric fields that can drive a system from a known initial vibrational eigenstate to a specified final quantum state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, Crank-Nicolson propagation, and gradient ascent optimization.

Fluoroalkylether substances (ether PFAS) constitute a large group of emerging PFAS with uncertain environmental fate. Among them, GenX is the well-known alternative to perfluorooctanoic acid and one of the six proposed PFAS to be regulated by the U.S. Environmental Protection Agency. This study investigated the structure-biodegradability relationship for 12 different ether PFAS with a carboxylic acid headgroup in activated sludge communities. Only polyfluorinated ethers with at least one -CH₂- moiety adjacent to or a C=C bond in the proximity of the ether bond underwent active biotransformation via oxidative and hydrolytic O-dealkylation. The bioreactions at ether bonds led to the formation of unstable fluoroalcohol intermediates subject to spontaneous defluorination. We further demonstrated that this aerobic biotransformation/defluorination could complement the advanced reduction process in a treatment train system to achieve more cost-effective treatment for GenX and other recalcitrant perfluorinated ether PFAS. These findings provide essential insights into the environmental fate of ether PFAS, the design of biodegradable alternative PFAS, and the development of cost-effective ether PFAS treatment strategies.

Key message

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO₂) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.