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Experimental and computational electronic circular dichroism spectra are used in conjunction with anisotropy spectra to chiroptically characterize mandelic acid and its derivatives into the vacuum UV.
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Carbon Storage BIL Workshop, Santa Fe, NM, May 1-3, 2023
This project investigates the accuracy of currently-used functionals in time-dependent density functional theory, which is today routinely used to predict and design materials and computationally model processes in solar energy conversion. The rigorously-based electron-ion dynamics method developed here sheds light on traditional methods and overcomes challenges those methods have. The fundamental research undertaken here is important for building reliable and practical methods for materials discovery. The ultimate goal is to use these tools for the computational design of new materials for solar cell devices of high efficiency.
The reV Routing (reVRt) tool is a computational framework for modeling and optimizing transmission infrastructure requirements for electrical grid connections. By employing a spatially-aware least-cost-path methodology, it allows users to incorporate a wide range of factors including siting constraints, regional component costs, land composition costs, point-of-interconnection costs, and network upgrade costs. Additionally, the tool enables advanced follow-on analyses, such as land characterization for potential transmission line routes, to support informed decision-making. Although it's designed to integrate seamlessly with the reV model, the reV Routing tool is versatile and can also be utilized independently for standalone analyses in transmission planning and resource assessment scenarios.
Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this article, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.
A myriad of perfluorinated compounds (PFCs) have the ability to interfere with steroidogenic acute regulatory (StAR) protein. Consequently, PFCs breaches cholesterol biotransformation in mitochondria and cause fatal consequences in steroidogenesis, however, these were poorly characterized. In the present study, we have evaluated toxic potencies, nuclear mediated probabilities and interaction profiles with StAR of PFCs using computational system biology tools. Toxicity endpoints revealed that PFCs contain high carcinogenicity, developmental toxicity, skin sensitization effects with low mutagenic activity. Consensus qualitative nuclear receptor agonist models show higher probability rates towards ER and PPAR-γ receptor than AR and AhR models were observed. To poise the subtle fluctuations of actual predictions, balanced accuracy and MCC were computed, and they signify perfect correlation ranges in all models. Screening studies resulting protein-ligand interaction profiles showed that residues Asn148, Asn150, Glu169, Ala171, Arg182, Phe184, Arg188, Trp241, Thr263 and Phe267 were identified as novel hotspots, participated in halogen bonds, H-bonds, atomic π-stacking, π-cation interactions and salt-bridges formation. Thus, the additional bonds contribute conformer stability that holds the protein structure at flexible state, so that PFCs acts as a barrier to cholesterol binding. From docking outcomes, representation space was created, that specifies high and medium StAR binders were occupied in toxic endpoints space with active concern. PFCs restrain molecular features and mitochondrial membrane disruption functions were revealed by efficient toxicogenomics studies. These data indicate toxicity and StAR protein binding levels of PFCs, sorted pinpoints could be useful in a promising way to know the other environmental pollutants and health risks. - Highlights: • In this study, QSAR models were revealed that PFCs have greater adverse toxicity along with nuclear receptors interaction potencies. • Binding potencies of PFCs against the StAR protein revealed only potential novel residue hotspots showed various bond patterns, and they sustain stability and complexity of StAR. • Through that, PFCs acts as a barrier in cholesterol binding and leads fatal consequences in steroidogenesis.
Orbital-free density functional theory constitutes a computationally highly effective tool for modeling electronic structures of systems ranging from room-temperature materials to warm dense matter. Its accuracy critically depends on the employed kinetic energy (KE) density functional, which has to be supplied as an external input. In this work we consider several nonlocal and Laplacian-level KE functionals and use an external harmonic perturbation to compute the static density response at T = 0 K in the linear and beyond-linear response regimes. We test for the satisfaction of exact conditions in the limit of uniform densities and for how approximate KE functionals reproduce the density response of realistic materials (e.g., Al and Si) against the Kohn-Sham DFT reference, which employs the exact KE. The results illustrate that several functionals violate exact conditions in the uniform electron gas (UEG) limit. We find a strong correlation between the accuracy of the KE functionals in the UEG limit and in the strongly inhomogeneous case. This empirically demonstrates the importance of imposing the limit of UEG response for uniform densities and validates the use of the Lindhard function in the formulation of kernels for nonlocal functionals. This conclusion is substantiated by additional calculations for bulk aluminum (Al) with a face-centered cubic (fcc) lattice and silicon (Si) with an fcc lattice, body-centered cubic (bcc) lattice, and semiconducting crystal diamond state. The analysis of fcc Al, and fcc as well as bcc Si data follows closely the conclusions drawn for the UEG, allowing us to extend our conclusions to realistic systems that are subject to density inhomogeneities induced by ions.
With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops—and a final concluding “World Café” on March 29, 2021—focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions—and collaborative parallel ideas—as the focus of work going forward to advance the field. These were then used to define short-term and longer-term “Blue Sky” goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing.
This report is documentation of the verification activities that have been performed on GALE-3.2 to ensure that the codes meet the requirements and specifications set forth by NRC and that the code meets its intended purpose. These activities are done in accordance with the GALE Software Quality Assurance Plan (PNNL-24249) and the GALE Configuration Management & Maintenance Plan (PNNL-24250). These two plans meet the requirements set for by both PNNL software quality assurance requirements and the NRC’s Software Quality Assurance Program and Guidelines (NUREG/BR-0167). The verification activities are documented to conform to Level 2 requirements of NUREG/BR-0167. Verifications were performed for both GALE-3.2 BWR and GALE-3.2 PWR Codes. The GALE 3.2 BWR and PWR codes are computerized mathematical models for calculating the releases of radioactive material in gaseous and liquid effluents. These computer codes are tools which can be used to determine conformance with the requirements of Appendix I to Title 10, Part 50 of the Code of Federal Regulations (10 CFR 50).
On the path toward the design of Li-ion batteries with increased energy densities, efforts are focused on the development of positive electrodes that can maximize the voltage of the full cell. However, the development of novel materials that operate at high voltage, while also showing high efficiency and meeting strict safety standards, is an ongoing challenge. LiCoPO 4 is being explored as a possible candidate, as the Co 2+/3+ redox couple operates at 4.8 V versus Li + /Li 0 . The presence of phosphate groups is typically expected to stabilize the compound against oxygen loss, yet the changes in Co-O bonding upon Li extraction have not been ascertained. In addition, LiCoPO 4 is riddled with problems relating to poor transport and strain in the crystal structure of the delithiated phase, which handicap its use as a high-voltage electrode. In this work, substituting ions to generate Li 1.025 Co 0.84 Fe 0.10 Cr 0.05 Si 0.01 (PO 4 ) 1.025 is found to stabilize both the electronic structure and crystal structure and, therefore, substantially improve the ability to fully utilize the redox capacity of the material. A thorough study by spectroscopic tools, combined with computations of the electronic structure, was used to probe changes in chemical bonding. The measurements revealed the existence of redox gradients between surface and bulk that are common in other materials that react at high potential. Lastly, the study offers a comprehensive understanding of the fundamental reactions in LiCoPO 4 -type frameworks, while further demonstrating that ion substitution is an effective tool for improving their performance.
NMACFoam is a computational fluid dynamics tool for simulating flow and solids transport in membrane-spacer channels pertaining to reverse osmosis or nanofiltration applications. It is developed on the OpenFOAM framework and allows for simulating spacers with complex geometries. It includes porous wall boundary conditions for membranes and can accurately capture concentration polarization effects. One can perform large calculations at module-scale domain sizes using high performance computing resources with NMACFoam, which scales to 1000s of processors for problems with 1-10 million cells.
Power engineers rely on computer-based simulation tools to assess grid performance and ensure security. At the core of these tools are solvers for sparse linear equations. When transformed into a bordered block-diagonal (BBD) structure, part of the sparse linear equation solving can be parallelized. This work focuses on using the Schur-complement-based method for LU factorization on BBD matrices, specifically, Jacobian matrices from large-scale systems. Our findings show that the natural ordering method outperforms the default ordering method in computational performance for each block of the BBD matrix. This observation is validated using synthetic 25k-bus and 70k-bus cases, showing a speedup of up to 38% when using natural ordering without permutation. Additionally, the impact of the number of partitions is studied, and the result shows that computational performance improves with more, smaller partitions in the BBD matrices.
This report is documentation of the verification activities that have been performed on GALE-3.1 to ensure that the codes meet the requirements and specifications set forth by NRC and that the code meets its intended purpose. Additional benchmark tests for GALE-3.1 are documented in Appendix A of NUREG-0016, Revision 2 for GALE-BWR 3.1 and Appendix A of NUREG-0017, Revision 2 for GALE-PWR 3.1. These activities are done in accordance with the GALE Software Quality Assurance Plan (PNNL-24249) and the GALE Configuration Management & Maintenance Plan (PNNL-24250). These two plans meet the requirements set for by both PNNL software quality assurance requirements and the NRC’s Software Quality Assurance Program and Guidelines (NUREG/BR-0167). The verification activities are documented to conform to Level 2 requirements of NUREG/BR-0167. Verifications were performed for both GALE-3.1 BWR and GALE-3.1 PWR Codes. The GALE 3.1 BWR and PWR codes are computerized mathematical models for calculating the releases of radioactive material in gaseous and liquid effluents. These computer codes are tools which can be used to determine conformance with the requirements of Appendix I to Title 10, Part 50 of the Code of Federal Regulations (10 CFR 50).
Automatic differentiation (AD) is a well-known technique for evaluating analytic derivatives of calculations implemented on a computer, with numerous software tools available for incorporating AD technology into complex applications. However, a growing challenge for AD is the efficient differentiation of parallel computations implemented on emerging manycore computing architectures such as multicore CPUs, GPUs, and accelerators as these devices become more pervasive. In this work, we explore forward mode, operator overloading-based differentiation of C++ codes on these architectures using the widely available Sacado AD software package. In particular, we leverage Kokkos, a C++ tool providing APIs for implementing parallel computations that is portable to a wide variety of emerging architectures. Here we describe the challenges that arise when differentiating code for these architectures using Kokkos, and two approaches for overcoming them that ensure optimal memory access patterns as well as expose additional dimensions of fine-grained parallelism in the derivative calculation. We describe the results of several computational experiments that demonstrate the performance of the approach on a few contemporary CPU and GPU architectures. We then conclude with applications of these techniques to the simulation of discretized systems of partial differential equations.
Offering broad-based technical expertise and world-class capabilities and facilities, NREL leverages these assets to provide the wind industry with a better understanding of fundamental physics, high-performance computing-enabled simulation tools, and the physical validation necessary to significantly lower the cost of wind energy. This report provides an overview of the many achievements NREL delivered on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during Fiscal Year (FY) 2019.
Biological threats continue to persist and evolve as an important challenge to national security. There are multiple ways in which novel viral pathogens could emerge to pose a serious threat to human health. This project developed a pathogen target identification tool that can rapidly respond to a novel or emerging viral biological threat. A set of computational tools were developed that provide detailed information on the newly sequenced genes, their protein products and the drug target sites for the proteins that are best suited for biological countermeasure development. Three key innovations were developed in the project. 1) Development of a new extensive database of protein pocket structures with structure-based search algorithms to rapidly link novel protein targets with the complete collection of previously experimentally solved protein structures. 2) A novel clustering pipeline was introduced to group matching structures and associated small-molecule binding ligands into a consensus protein pocket with the associated small-molecule chemotypes predicted to fit in the pocket site. The matching experimentally solved structures were used to inform the value of different target sites. 3) Where there are viral protein targets with pockets structurally matched to similar human proteins, a biological knowledge graph, which links molecular interactions with human disease, was used to further assess the potential negative impact of a viral protein target with similarities to human proteins that could have important off target side effects. In total, the project produced a new resource for rapid and detailed assessment of promising targets for countermeasures, reflecting the ongoing wet lab, clinical, and computational data being collected. These capabilities will improve the ability to respond to a biological threat in multiple domains.