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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗
DOE OSTI2025

Rapid Screening of Liquid Metal Wetting for a Materials Compatibility Library

Wetting behavior of molten metals on solid substrates is a critical phenomenon influencing numerous industrial applications, including welding, anti-corrosion coatings, and metal additive manufacturing (AM). In particular, molten metal jetting (MMJ), an emerging AM technology, requires that the molten metal remain pinned at the nozzle exit. Thus, each new metal requires a specific nozzle material to ensure consistent droplet ejection and deposition, making it important to rapidly identify the appropriate wetting combinations. However, traditional measurements of wetting angles require expensive equipment and only allow one combination of materials to be investigated at a time which can be time consuming. This work introduces a rapid screening method based on sessile droplet experiments to evaluate wetting profiles across multiple metal–substrate combinations simultaneously. This study investigates the wetting interactions of molten Al alloy (Al4008), Cu, and Sn on various ceramic and metal substrates to identify optimal material combinations for MMJ nozzle designs. Results demonstrate that Al4008 achieves wetting on ceramic substrates such as AlN, TiO 2 , and SiC, with varying mechanisms including chemical reactions and weak surface interactions. Additionally, theoretical predictions regarding miscibility gaps and melting point differences were verified for Cu and Sn on refractory metals like Mo and W. Findings from this study contribute to the establishment of a materials compatibility library, enabling the selection of wetting/non-wetting combinations for stable MMJ operation. This resource not only advances MMJ technologies but also provides valuable insights for broader applications such as welding, coating, and printed electronics.

Materials science↗
DOE OSTI2025

Elucidating molecular scale interactions underlying the freezing behavior of salt solutions in silica nanopores

Elucidating the influence of nanoscale confinement on the freezing behavior of salt solutions is of fundamental interest to environmental security and materials science and engineering. Specific structural information such as the coordination environment of ions in confined aqueous medium, effects on the extended hexagonal network of water molecules and their mutual non-bonding interactions in confinement are sparse in the literature along with the change in the dynamical characteristics of water in the presence of ions. To address these knowledge gaps, the current study is focused on investigating the influence of reduced dimensionality arising from nanoscale confinement on the structural evolution of salt solutions on freezing and the associated fluid–surface interactions. In this regard, operando wide-angle X-ray scattering (WAXS) measurements and classical molecular dynamics (MD) simulations are conducted with water and 0.5 M CaCl 2 , MgCl 2 and KCl solutions confined in 4 nm sized SBA-15 silica pores upon cooling from 300 K to 200 K. The freezing point of the salt solutions is depressed to 235 K which is about 10 K lower than that of confined water. The translational dynamics of confined salt solutions indicates shifts in the fragile-to-strong dynamical crossover to a lower temperature compared to pure water. The strong electrostatic attraction between the cations and the surrounding water molecules contributes to the freezing point depression in confined salt solutions. These insights unlock the molecular-scale basis and mechanisms underlying the freezing behavior of confined salt solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Plasmon Dynamics Driven by Aggregation of Tris(2,2′-bipyridine)ruthenium(II)-Functionalized Gold Nanoparticles Probed by XANES and Transient Absorption Spectroscopy

Energy conversion dynamics is critical for advancing next-generation photovoltaics, optoelectronics, and light-harvesting technologies. Noble metal plasmonic nanoparticles play a pivotal role as nanoscale electromagnetic confinement structures, driving photon-induced chemical reactions. In this study, we explore the effects of [Ru(bpy) 3 ] 2+ functionalization and aggregation on citrate-capped gold nanoparticles (AuNPs) of 40 and 100 nm diameters, focusing on molecule-plasmon interactions and their influence on electronic and energy dissipation properties. X-ray absorption near-edge spectroscopy (XANES) revealed that [Ru(bpy) 3 ] 2+ functionalization induces controlled aggregation without altering the oxidation state of gold. A more pronounced white-line intensity is observed in 40 nm AuNPs, consistent with greater s–p–d hybridization and a higher density of surface states, likely influenced by both nanoparticle size and aggregation. Transient absorption (TA) spectroscopy highlights faster electron–phonon relaxation dynamics in aggregated 40 nm nanoparticles, which is attributed to increased electron delocalization and more efficient coupling to the phonon bath. In contrast, 100 nm nanoparticles exhibit minimal changes due to a lower degree of aggregation. Interestingly, we observe that enhanced electron–phonon coupling in aggregated nanoparticles coincides with a slowing of electron–electron scattering. These observations suggest a competitive interplay between the two relaxation pathways, where enhanced energy transfer to the lattice in aggregated systems can suppress electronic thermalization. In conclusion, these findings underscore the critical role of nanoparticle size, aggregation, and molecule–surface interactions in modulating plasmonic dynamics and excited-state lifetimes and further provide valuable insights into designing tailored plasmonic systems with transformative potential for sensing, catalysis, and energy conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers

Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.

X-ray scattering↗
DOE OSTI2024

Simulation of plasma and neutral transport in PISCES-RF using SOLPS-ITER

In this research, the fluid plasma transport code SOLPS-ITER is applied and validated against experimental data from the plasma interaction surface component experimental station (PISCES)-RF linear plasma device to establish a physics basis for plasma and neutral transport in its two magnetic field (B-field) geometry setups-(1)the cusp and (2) non-cusp or linear B-field. The main focus of this study is to understand (1) radial plasma transport (2) heat and particle loads on the upstream dump and downstream target plate, and (3) the physics of plasma-neutral interactions in PISCES-RF. The simulation setup adheres to typical PISCES-RF experimental conditions, with a 2D helicon power deposition profile as an input heating source. SOLPS-ITER simulations reproduce experimental conditions with the Bohm diffusion model for both B-field configurations of the PISCES-RF experiment. Major energy loss channels include neutral radiation and power deposited on the wall and dump plate, with only 1% of the input power reaching the target. The ionization front is well confined near the dump plate due to the heating and puffing regions. Additionally, SOLPS-ITER simulation results are also found to be in very good agreement with the particle-in-cell calculations using the code-PICOS++ which supports the validity of SOLPS in low collisionality regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
DOE OSTI2025

Fouling behavior of zwitterionic membranes compared to polyamide membranes

Membrane fouling remains a critical bottleneck for reverse osmosis (RO) desalination, driving energy consumption and reducing membrane lifetime. Here, we employ all-atom molecular dynamics simulations to investigate the antifouling behavior of random zwitterionic amphiphilic copolymer (r-ZAC) membranes composed of sulfobetaine methacrylate (SBMA) and allyl methacrylate (AMA), benchmarked against conventional polyamide (PA) RO membranes. Structural and dynamical analyses—including radial distribution functions, coordination numbers, tetrahedral order parameters, vector orientation, and residence-time correlation functions—reveal that r-ZAC surfaces sustain tightly bound, long-lived hydration layers with preserved tetrahedrality and anisotropic water orientation, in sharp contrast to the weak and disordered hydration of PA. Steered molecular dynamics simulations demonstrate that r-ZAC membranes impose substantial free-energy barriers to foulant approach (alginate ≈ 90 kcal/mol, sucrose ≈ 35 kcal/mol, humic acid ≈ 15 kcal/mol), whereas PA membranes exhibit negligible barriers (< 1 kcal/mol) and thermodynamically favorable adsorption. Detailed foulant–surface interaction analyses show that zwitterionic hydration and electrostatic heterogeneity in r-ZAC suppress adhesion, except in the case of amphiphilic humic acid, which exploits multiple binding modes. Together, these results establish molecular-level design principles for antifouling membranes: the combination of zwitterionic hydration, structured interfacial water, and controlled amphiphilic balance in r-ZAC membranes provides superior resistance to organic fouling relative to PA.

Cross-linked polyamide↗
DOE OSTI2026

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗
DOE OSTI2026

Single-Molecule Tracking Measurements Reveal the Detailed Mechanisms of Molecular Diffusion in Solvent Mixtures under Nanoconfinement

Understanding mass transport mechanisms in nanopores is important for developing advanced materials for chemical separations, chemical sensing, and energy storage. This paper reports a novel imaging platform that is employed for the first time to investigate the detailed diffusion dynamics of single rhodamine B (RhB) dye molecules confined within solution-filled cylindrical anodic aluminum oxide (AAO) nanopores. The imaging platform relies on illumination of horizontally-oriented AAO nanopores in a highly inclined and laminated optical (HILO) light sheet microscopy geometry. The method was used to investigate the translational and orientational dynamics of single rhodamine B (RhB) molecules within horizontally-oriented 5- and 10-nm diameter AAO nanopores filled with water–ethanol mixtures. The established platform enabled the observation of one-dimensional motion along the pore axis involving occasional short- or long-term immobilization at the single-molecule level. Analysis of cumulative squared-displacement distributions revealed fast (5 – 30 µm²/s), intermediate (1 – 5 µm²/s), and slow (< 1 µm²/s) diffusion components. From the effects of mixture composition and pore size on the contributions of these three components, we inferred that the fast, intermediate, and slow components could be assigned to desorption-mediated hopping, crawling, and wiggling motions, respectively. The platform based on the horizontally-oriented AAO nanopores also permitted single-molecule emission polarization measurements that revealed the negligible steric confinement of individual diffusing RhB molecules within the AAO nanopores. The imaging platform based on AAO membranes and HILO microscopy provided a unique means to investigate how solvation-mediated surface interactions and nanoconfinement govern molecular transport in nanoporous environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unlocking expanded flagellin perception through rational receptor engineering

Abstract The surface-localized receptor kinase FLS2 detects the flg22 epitope from bacterial flagella. FLS2 is conserved across land plants, but bacterial pathogens exhibit polymorphic flg22 epitopes. Most FLS2 homologues possess narrow perception ranges, but four with expanded perception have been identified. Using diversity analyses, AlphaFold modelling and amino acid properties, key residues enabling expanded recognition were mapped to FLS2’s concave surface, interacting with the co-receptor and polymorphic flg22 residues. Synthetic biology enabled engineering of expanded recognition from QvFLS2 (Quercus variabilis) into a homologue with canonical perception. A similar approach enabled transfer ofAgrobacteriumperception from FLS2 XL (Vitis riparia) into VrFLS2. Evolutionary analyses across three plant orders showed residues under positive selection aligning with those binding the co-receptor and flg22’s C terminus, suggesting more alleles with expanded perception exist. Our experimental data enabled the identification of specific receptor amino acid properties and AlphaFold3 metrics that facilitate predicting FLS2–flg22 recognition. This study provides a framework for rational receptor engineering to enhance pathogen restriction.

Plant Sciences↗
DOE OSTI2025

Surrogate models to optimize plasma-assisted atomic layer deposition in high aspect ratio features

In this work, we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching (ALE), surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma–surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to ALE and more complex structures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Impurity transport in PISCES-RF

Linear plasma devices (LPD) utilizing a helicon plasma source, a high density light ion source, can generate impurities due to progressive erosion of the radio frequency (RF) transmission window caused by rectified sheath voltage. These source-born impurities can entrain and be transported by the plasma toward a target, affecting plasma-material interaction studies. Earlier work on material testing in Prototype-Materials Plasma Exposure eXperiment at ORNL revealed significant source impurity deposition on downstream targets. However, using a similar RF source, no target impurity deposition is observed in Plasma Interaction Surface Component Experimental Station (PISCES)-RF despite evidence of RF window erosion in the source region, thereby motivating the present work. Experimentally, using various magnetic field configurations upstream of the PISCES-RF plasma source and seeding titanium (Ti) impurities at various axial locations, impurity transport and deposition along the machine axis were investigated. It was found that Ti deposition was localized to the side of the plasma source where the Ti impurity was seeded. In contrast, aluminum (Al) deposition, originating from the sputtering of the helicon window, occurred predominantly upstream of the plasma source, suggesting an asymmetry in the axial transport of eroded RF window material. These observations suggest a stagnation of the parallel plasma flow immediately downstream of the plasma source, with impurity ions remaining unmagnetized near the source upstream. Al deposition in magnetic field-free regions in PISCES-RF indicates that sputtered Al impurities likely remained neutral due to their large ionization mean-free path under PISCES-RF conditions. Plasma modeling and simulation supported this, indicating that Al-neutrals transport toward the helicon source upstream for low electron density cases. It was found that the Larmor radius of the Al ions was greater than the plasma radius towards the source upstream and remained weakly magnetized in PISCES-RF, meaning that plasma source-born impurities are not efficiently entrained in the plasma flow. These findings provide critical insights into impurity transport in helicon plasma-based LPDs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Integrated modeling of RF-induced tungsten erosion at ICRH antenna structures in the WEST tokamak *

This paper introduces STRIPE (Simulated Transport of RF Impurity Production and Emission), an advanced modeling framework developed to analyze material erosion and the global transport of eroded impurities originating from radio-frequency (RF) antenna structures in magnetic confinement fusion devices. STRIPE integrates multiple physics modules: SolEdge3x for scrape-off-layer plasma profiles, COMSOL for 3D RF rectified sheath potentials, RustBCA for erosion yields and surface interactions, and global impurity transport for 3D ion energy-angle distributions and impurity transport. The framework is applied to an ion cyclotron RF-heated L-mode discharge (#57877) in the WEST tokamak, where it predicts a thirty-fold increase in gross tungsten erosion at antenna limiters during the transition from ohmic to ICRH operation. Additionally, under ICRH conditions, a tenfold enhancement in erosion is observed when comparing RF sheath effects to purely thermal sheath conditions. High-charge-state oxygen ions ($\mathrm{O}$ 6+ and above) are identified as the dominant contributors to tungsten sputtering. To validate the model, a synthetic diagnostic tool based on inverse photon efficiency (S/XB coefficients) from the ColRadPy collisional-radiative model enables direct comparison with spectroscopic measurements. Model predictions using a plasma composition of 1% oxygen and 99% deuterium show good agreement with observed W − I (400.9 nm) emission for discharge #57877, supporting the accuracy of the STRIPE framework. This study focuses specifically on gross erosion calculations to demonstrate STRIPE’s capabilities. Future extensions of this work will incorporate net erosion, re-deposition, self-sputtering effects, and whole-device modeling of sputtered tungsten impurity transport. STRIPE is also being applied to other RF-heated linear and toroidal devices, offering valuable insights for antenna design, impurity control, and performance optimization in next-generation fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Plasma processing of SRF cavities at Jefferson Lab: Experiment results and simulation insight

Plasma processing of superconducting radio frequency (SRF) cavities has been an active research effort at Jefferson Lab (JLab) since 2019, aimed at enhancing cavity performance by removing hydrocarbon contaminants and reducing field emission. In this experiment, processing using argon-oxygen and helium-oxygen gas mixtures to find minimum ignition power at different cavity pressure was investigated. Ongoing simulations are contributing to a better understanding of the plasma surface interactions and the fundamental physics behind the process. These simulations, combined with experimental studies, guide the optimization of key parameters such as gas type, RF power, and pressure to ignite plasma using selected higher-order mode (HOM) frequencies. This paper presents experimental data from argon-oxygen and helium-oxygen gas mixture C75 and C100 cavity plasma ignition studies, as well as simulation results for the C100-type cavity based on the COMSOL model previously applied to the C75 cavity.

Accelerator Physics↗
DOE OSTI2024

Krash-ESL-Final report

During the period of the current Grant, the effort of the UCSD researchers was focused on: i) The application of the gyrokinetic code COGENT code to study the kinetic drift wave instability and its impact on blob dynamics, and ii) The modeling of edge plasma transport in collisional and semi-collisional regimes, and iii) Impurity transport in edge plasma and the incorporation of plasma-impurity collision operator (hereafter unlike particle collision operator) into COGENT. Over the years four UCSD graduate students (J. Angus, W. Lee, Y. Zhang, and A. Knyazev), funded under this Grant, contributed to the COGENT development/utilization and defended their PhD theses, which are based, partly, on their research related to the COGENT. Three of them (J. Angus, W. Lee, and Y. Zhang) have received “The Best MAE Graduate Student of the Year” awards. Whereas W. Lee after defending his PhD thesis returned back to his home country (South Korea), J. Angus, and Y. Zhang are continuing their research in plasma physics in the LLNL and LANL, whereas A. Knyazev currently is a postdoctoral fellow at Columbia University working on the theoretical aspects of advanced stellarators. The results obtained in our studies were disseminated in many papers published in first-class refereed scientific journals and presented on prestigious scientific meetings including the Annual APS DPP Meeting, Sherwood Fusion Theory Conference, IAEA Fusion Energy Conferences, International Conferences on Plasma-Surface Interactions in Controlled Fusion Devices, International Workshops on Edge Plasma Theory in Fusion Devices, etc. In what follows we highlight the major accomplishments of the UCSD Team in the main areas of our research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗
DOE OSTI2024

Software Quality Assurance Plan ANSYS LSDYNA Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic and electromagnetic simulation capabilities. ANSYS LS-DYNA is the most used explicit simulation program capable of simulating the response of materials to short periods of severe loading. Its many elements, contact formulations, material models, and other controls can be used to simulate complex models with control over all the details of the problem. ANSYS LS-DYNA has a vast array of capabilities to simulate extreme deformation problems using its explicit solver. Engineers can tackle simulations involving material failure and look at how the failure progresses through a part or through a system. Models with large amounts of parts or surfaces interacting with each other are also easily handled, and the interactions and load passing between complex behaviors are modeled accurately. Using computers with higher numbers of CPU cores can drastically reduce solution times. In addition, many consulting firms and hundreds of universities use ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. ANSYS has successfully passed over 100 customer quality system audits against American Society of Mechanical Engineers (ASME) NQA-1 and 10 CFR Part 50, Appendix B, since the company was founded, over 60 of which have been since 1997. ANSYS has successfully passed over 100 International Organization for Standardization (ISO) 9001 assessments. ANSYS design analysis software is the first created within a quality system with ISO 9001 certification, which is the internationally accepted quality standard. Product development, testing, maintenance, and support processes also meet the US Nuclear Regulatory Commission’s (NRC’s) quality requirements, as they have for nearly four decades. ANSYS staff perform more than 60,000 software verification tests before releasing each new product. ASME NQA-1-2012 (Subpart 2.7 is specific to software) is the industry- and NRC-accepted approach (consensus standard) for meeting 10 CFR Part 50, Appendix B, requirements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗