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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Recent developments in the PySCF program package

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. Here, we explain the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

PyFLOSIC: Python-based Fermi–Löwdin orbital self-interaction correction

We present pyflosic, an open-source, general-purpose python implementation of the Fermi–Löwdin orbital self-interaction correction (FLO-SIC), which is based on the python simulation of chemistry framework (pyscf) electronic structure and quantum chemistry code. Thanks to pyscf, pyflosic can be used with any kind of Gaussian-type basis set, various kinds of radial and angular quadrature grids, and all exchange-correlation functionals within the local density approximation, generalized-gradient approximation (GGA), and meta-GGA provided in the libxc and xcfun libraries. A central aspect of FLO-SIC is the Fermi-orbital descriptors, which are used to estimate the self-interaction correction. Importantly, they can be initialized automatically within pyflosic; they can also be optimized within pyflosic with an interface to the atomic simulation environment, a python library that provides a variety of powerful gradient-based algorithms for geometry optimization. Although pyflosic has already facilitated applications of FLO-SIC to chemical studies, it offers an excellent starting point for further developments in FLO-SIC approaches, thanks to its use of a high-level programming language and pronounced modularity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Accurate Hellmann–Feynman forces from density functional calculations with augmented Gaussian basis sets

The Hellmann–Feynman (HF) theorem provides a way to compute forces directly from the electron density, enabling efficient force calculations for large systems through machine learning (ML) models for the electron density. The main issue holding back the general acceptance of the HF approach for atom-centered basis sets is the well-known Pulay force which, if naively discarded, typically constitutes an error upward of 10 eV/Å in forces. In this work, we demonstrate that if a suitably augmented Gaussian basis set is used for density functional calculations, the Pulay force can be suppressed, and HF forces can be computed as accurately as analytical forces with state-of-the-art basis sets, allowing geometry optimization and molecular dynamics to be reliably performed with HF forces. Our results pave a clear path forward for the accurate and efficient simulation of large systems using ML densities and the HF theorem.

Pathak, Shivesh (ORCID:0000000172798195)↗
DOE OSTI2024

GPAW: An open Python package for electronic structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Speculations on the W-mass measurement at CDF*

Abstract The W mass determination at the Tevatron CDF experiment reported a deviation from the SM expectation at the 7 σ level. We discuss a few possible interpretations and their collider implications. We perform electroweak global fits under various frameworks and assumptions. We consider three types of electroweak global fits in the effective-field-theory framework: the S - T , S - T - , and eight-parameter flavor-universal one. We discuss the amounts of tensions between different measurements reflected in these fits and the corresponding shifts in central values of these parameters. With these electroweak fit pictures in hand, we present a few different classes of models and discuss their compatibility with these results. We find that while explaining the discrepancy, the single gauge boson extensions face strong LHC direct search constraints unless the is fermiophobic (leptophobic), which can be realized if extra vector fermions (leptons) mix with the SM fermions (leptons). Vector-like top partners can partially generate the needed shift to the electroweak observables. The compatibility with the top squark is also studied in detail. We find that the non-degenerate top squark soft masses enhance the needed operator coefficients, enabling an allowed explanation compatible with current LHC measurements. Overall, more theoretical and experimental developments are highly in demand to reveal the physics behind this discrepancy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Beam-driven dynamics of aluminium dopants in graphene

Abstract Substituting heteroatoms into graphene can tune its properties for applications ranging from catalysis to spintronics. The further recent discovery that covalent impurities in graphene can be manipulated at atomic precision using a focused electron beam may open avenues towards sub-nanometer device architectures. However, the preparation of clean samples with a high density of dopants is still very challenging. Here, we report vacancy-mediated substitution of aluminium into laser-cleaned graphene, and without removal from our ultra-high vacuum apparatus, study their dynamics under 60 keV electron irradiation using aberration-corrected scanning transmission electron microscopy and spectroscopy. Three- and four-coordinated Al sites are identified, showing excellent agreement with ab initio predictions including binding energies and electron energy loss spectrum simulations. We show that the direct exchange of carbon and aluminium atoms predicted earlier occurs under electron irradiation, although unexpectedly it is less probable than the same process for silicon. We also observe a previously unknown nitrogen–aluminium exchange that occurs at Al–N double-dopant sites at graphene divacancies created by our plasma treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A deep neural network to search for new long-lived particles decaying to jets

A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length $\mathrm{c}\tau_0$ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Rare and Experimentally Challenging Supersymmetry Signatures

Supersymmetry has long played a central role in the search for physics beyond the Standard Model at colliders, providing a comprehensive and internally consistent framework for generating well-motivated experimental signatures. For more than 15 years of Large Hadron Collider (LHC) operation, the CMS and ATLAS Collaborations have achieved remarkable sensitivity to a wide range of supersymmetric signatures. Despite this unprecedented reach, no conclusive evidence for supersymmetry has emerged. If supersymmetry is nature's solution to outstanding questions in particle physics, it is necessarily challenging to find. In this article, we review supersymmetric signatures that are particularly rare or otherwise challenging, with a focus on searches at the LHC. We highlight experimental challenges related to detector constraints and analysis difficulties, in addition to model challenges in the interpretation and optimization of searches. Here, we also identify regions of signature space that remain comparatively unconstrained and therefore represent promising targets for future exploration.

Large Hadron Collider↗
DOE OSTI2026

V-HAMSTeR v1.0.0

V-HAMSTeR is a bioinformatics software tool designed to predict the hosts of viruses directly from genomic sequences. It can be used by researchers to predict animal, prokaryotic, plant, protist or fungal viral hosts including viruses that may be fragmented or discovered in environmental metagenomic datasets. Features & Uses: The software employs a novel dual-stream deep learning architecture that dynamically fuses implicit sequence embeddings from a genomic foundation model with 13 explicit, handcrafted biological features (e.g., coding density and strand switch rates). To ensure maximum reliability, V=HAMSTeR deploys a 5-fold deep ensemble calibrated via Joint Temperature Scaling, providing users with statistically rigorous confidence probabilities. It also features an automated sequence chunking and mean-pooling module to seamlessly process variable-length contigs. Advantages Over Similar Technologies: Existing tools (e.g., IPEV, RNAVirHost) typically rely on either basic k-mers or isolated neural networks. V-HAMSTeR's hybrid architecture captures both broad genomic context and specific biological motifs that standalone foundation models often miss. Furthermore, unlike competitor tools that struggle with incomplete data or exhibit extreme overconfidence, V-HAMSTeR is explicitly benchmarked and mathematically calibrated for fragmented assemblies (1kb–10kb). This makes it uniquely robust, accurate, and trustworthy for the messy reality of real-world environmental viromics.

Grigson, Susie [Lawrence Berkeley National Laborat↗
DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Probing Naturalness with Searches for Supersymmetric Higgs Partners at the Large Hadron Collider

The discovery of the Higgs boson at the Large Hadron Collider completed the standard model of particle physics and led to the 2013 Nobel Prize. While it has been extremely successful in describing a wide variety of phenomena, the standard model cannot be the final theory of nature because it is unable to explain why the Higgs boson mass lies at the electroweak scale rather than 16 orders of magnitude larger at the Planck scale, a theoretical flaw known as the ``hierarchy problem.'' Supersymmetry is a standard model extension that could provide a natural solution to the hierarchy problem by introducing light supersymmetric Higgs partners. This proposal focused on searches that are sensitive to supersymmetric Higgs partners in data collected by the ATLAS experiment, which were carried out by the University of Illinois high-energy physics group led by PI Ben Hooberman as part of his Department of Energy Early Career Research Program award from 2017-2023. The searches were complemented by improvements to particle identification algorithms and the ATLAS trigger system that extend the sensitivity of future supersymmetry searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

An Environmental and Societal Analysis of the US Electrical Energy Industry Based on the Water–Energy Nexus

To meet rising energy demands, power plant operations will expand, influencing the interactions between the water–energy nexus and society. However, a major challenge is integration of social dimensions within electricity generation. To address this, we generate a baseline dataset using US public data (2014–2019) from the Energy Information Administration and US Bureau of Labor Statistics. We identify the rate of energy consumed, CO 2 , SO 2 and NO x emissions generated, and water used per MWh net electricity as well as employee wellbeing per unit MW capacity during electricity generation. Rates of energy consumption (MMBtu/MWh) decreased 4.9%, but water consumption and withdrawal (m 3 /MWh) both increased 0.93% and 0.31%, respectively. Emissions of CO 2 , SO 2 and NO x decreased 22.64%, 75% and 25% MT/MWh, respectively. Thermoelectric cooling withdrawal and consumption is led by natural gas (50.07%, 38.31%), coal (29.61%, 25.07%), and nuclear energies (13.55%, 18.99%). Electric power generation contributes 0.06 injuries–illnesses/TWh and 0.001 fatalities/TWh, of which fossil fuels contributed 70% and 15%, respectively. Fossil fuels led in average annual employment (0.02 employees/MW) with low cost salaries (USD 0.09/MW) likely due to high collective capacity, which is declining. Estimated rates in this study and framework will aid power industry transition and operational decision makers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Genomic Diversity and Phenotypic Variation in Fungal Decomposers Involved in Bioremediation of Persistent Organic Pollutants

Fungi work as decomposers to break down organic carbon, deposit recalcitrant carbon, and transform other elements such as nitrogen. The decomposition of biomass is a key function of wood-decaying basidiomycetes and ascomycetes, which have the potential for the bioremediation of hazardous chemicals present in the environment. Due to their adaptation to different environments, fungal strains have a diverse set of phenotypic traits. This study evaluated 320 basidiomycetes isolates across 74 species for their rate and efficiency of degrading organic dye. We found that dye-decolorization capacity varies among and within species. Among the top rapid dye-decolorizing fungi isolates, we further performed genome-wide gene family analysis and investigated the genomic mechanism for their most capable dye-degradation capacity. Class II peroxidase and DyP-type peroxidase were enriched in the fast-decomposer genomes. Gene families including lignin decomposition genes, reduction-oxidation genes, hydrophobin, and secreted peptidases were expanded in the fast-decomposer species. This work provides new insights into persistent organic pollutant removal by fungal isolates at both phenotypic and genotypic levels.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1997

Multilevel methods for elliptic problems on unstructured grids

Multilevel methods on unstructured grids for elliptic problems are reviewed. The advantages of these techniques are the flexible approximation of the boundaries of complicated physical domains and the ability to adapt the grid to the resolution of fine scaled structures. Multilevel methods, which include multigrid methods and domain decomposition methods, depend on the correct splitting of appropriate finite element spaces. The standard splittings used in the structured grid case cannot be directly extended to unstructured grids due to their requirement for a hierarchical grid structure. Issues related to the application of multilevel methods to unstructured grids are discussed, including how the coarse spaces and transfer operators are defined and how different types of boundary conditions are treated. An obvious way to generate a coarse mesh is to regrid the physical domain several times. Several alternatives are proposed and discussed: node nested coarse spaces, agglomerated coarse spaces and algebraically generated coarse spaces.

Chan, Tony F.↗