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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Diffusional creep in UO 2 informed by lower length scale simulations

Using molecular dynamics, we predict information at the atomistic scale used to develop a mechanistic UO 2 creep model for use in higher length-scale fuel performance codes. The ultimate objective of the model is to not only to capture the creep rates of UO 2 but to determine the dominant mechanism in the diffusional regime, which is still debated in the literature. It is important to have a model to capture the correct mechanisms for creep in UO 2 as this can be used as the foundation when applying to other fuels, such as doped UO 2 , and when irradiation is accounted. In last years NEAMS milestone (FY22), we developed a prelimnary model, however there were issues, for example, excessively high values of uranium vacancy concentrations at the grain boundary. This year we have addressed the issues with the previous version of the model, added a new term that accounts for the nucleation of dislocations at stress raisers (e.g., triple junctions) within the microstructure and discussed where there was disagreement in the literature about the underpinning physics (uranium self-diffusion at the grain boundary).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Nuclear–Electronic Orbital Quantum Mechanical/Molecular Mechanical Real-Time Dynamics

Simulating the nuclear–electronic quantum dynamics of large-scale molecular systems in the condensed phase is key for studying biologically and chemically important processes such as proton transfer and proton-coupled electron transfer reactions. Herein, the real-time nuclear–electronic orbital time-dependent density functional theory (RT-NEO-TDDFT) approach is combined with a hybrid quantum mechanical/molecular mechanical (QM/MM) strategy to enable the accurate description of coupled nuclear–electronic quantum dynamics in the presence of heterogeneous environments such as solvent or proteins. The densities of the electrons and quantum protons are propagated in real time, while the other nuclei are propagated classically on the instantaneous electron–proton vibronic surface. This approach is applied to phenol bound to lysozyme, intramolecular proton transfer in malonaldehyde, and nonequilibrium excited-state intramolecular proton transfer in o-hydroxybenzaldehyde. Furthermore, these examples illustrate that the RT-NEO-TDDFT framework, coupled with an atomistic representation of the environment, allows the simulation of condensed-phase systems that exhibit significant nuclear quantum effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Atomistically-informed modeling of point defect clustering and evolution in irradiated ThO 2

A cluster dynamics (CD) model has been developed to investigate the nucleation and growth of point defect clusters, i.e., interstitial prismatic loops and nanoscale and sub-nanoscale voids, in ThO 2 during irradiation by energetic particles. The model considers cluster off-stoichiometry due to the asymmetry of point defect generation on the O and Th sublattices under irradiation, as well as the point defect diffusivities and the defect binding energies to clusters. The energies were established using detailed molecular dynamics simulations considering the statistical variability of cluster configuration. A high-order adaptive time-integration has been used to solve the model. The predicted loop density and their average size is in good agreement with reported experimental observations for proton irradiated ThO 2 at 600°C. In conclusion, the model did not predict void evolution due to the sluggish kinetics of cation vacancies, explaining the absence of voids in proton irradiated ThO 2 (and other oxides) at relatively low temperatures.

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

Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations

Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.

BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗
DOE OSTI2022

Quantifying the atomistic free-volume morphology of materials with graph theory

Here, we introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage workflow, by which spheres are iteratively grown inside of voxels, and ultimately converted to planar graphs, which are then characterized via a graph-based order parameter. Our approach is computationally efficient, physically intuitive, and universally transferable to any material system. Validation of our methodology is performed on several sets of materials problems: (1) classification of unique free volumes in various crystal phases, (2) autonomous detection and classification of complex surface defects during epitaxial growth simulations, (3) characterization of free volume defects in metals/alloys, and (4) quantification of the spatio-temporal behavior of nano-scale free volume morphologies as a function of both temperature and free-volume size. Our method accurately identifies and characterizes unique free volumes over a multitude of systems and length scales, indicating its potential for future use in understanding the relationship between free volume morphology and material properties under both static and dynamic conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural water in amorphous carbonate minerals: ab initio molecular dynamics simulations of X-ray pair distribution experiments

Water is known to play a controlling role in directing mineralization pathways and stabilizing metastable amorphous intermediates in hydrous carbonate mineral MCO 3 ·nH 2 O systems, where M 2+ is a divalent metal cation. Despite this recognition, the nature of the controls on crystallization is poorly understood, largely owing to the difficulty in characterizing the dynamically disordered structures of amorphous intermediates at the atomic scale. Here, we present a series of atomistic models, derived from ab initio molecular dynamics simulation, across a range of experimentally relevant cations (M = Ca, Mg, Sr) and hydration levels (0 ≤ n ≤ 2). Theoretical simulations of the dependence of the X-ray pair distribution function on the hydration level n show good agreement with available experimental data and thus provide further evidence for a lack of significant nanoscale structure in amorphous carbonates. Upon dehydration, the metal coordination number does not change significantly, but the relative extent of water dissociation increases, indicating that a thermodynamic driving force exists for water dissociation to accompany dehydration. Mg strongly favors monodentate conformation of carbonate ligands and shows a marked preference to exchange monodentate carbonate O for water O upon hydration, whereas Ca and Sr exchange mono- and bidentate carbonate ligands with comparable frequency. Finally, water forms an extensive hydrogen bond network among both water and carbonate groups that exhibits frequent proton transfers for all three cations considered suggesting that proton mobility is likely predominantly due to water dissociation and proton transfer reactions rather than molecular water diffusion.

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

Learning Grain-Boundary Segregation: From First Principles to Polycrystals

The segregation of solute atoms at grain boundaries (GBs) can strongly impact the structural and functional properties of polycrystals. Yet, due to the limited availability of simulation tools to study polycrystals at the atomistic scale (i.e., interatomic potentials), there is a minimal understanding of the variation of solute segregation tendencies across the very complex space of GB microenvironments and the large range of alloys in which it can occur. Here, we develop an algorithmic framework that can directly learn the full spectrum of segregation energies for a metal solute atom in a metal polycrystal from ab initio methods, bypassing the need for alloy interatomic potentials. This framework offers a pathway to a comprehensive catalog of GB solute segregation with quantum accuracy, for the entire alloy space. As an initial demonstration in this pursuit, we build an extensive GB segregation database for aluminum-based alloys across the periodic table, including dozens of alloys for which there are substantially no prior data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Advancing Multiscale Simulation of Plasma-Surface Interfaces

We report the development of an atomistic-informed, surface-state-dependent predictive model for particle exchange in a carbon-tungsten plasma-surface interface. The predictive model uses machine learning (ML) techniques to learn the energy and angular distributions for particle exchange and rate functions for surface state evolution from molecular dynamics simulations of cumulative bombardment of tungsten by energetic carbon ions. Each predictive component is sensitive to the energy and trajectory of incident plasma species and the surface state. The surface state is represented by a set of surface state descriptors, which were derived from the atomistic surface state for each independent carbon bombardment event. These descriptors are representative of the composition and degree of amorphization of the outermost angstrom of surface material and were chosen to optimize predictive performance for particle exchange at the interface. The distributions for particle exchange (reflection/sputtering) are demonstrated to vary with each surface state descriptor, motivating the development of surface-state-dependent particle exchange models for plasma simulations. The performance of various ML methods was compared, including polynomial quantile regression, artificial neural networks, k-nearest neighbors, and random forest algorithms, with polynomial regression performing the best for interpolation and extrapolation of learned relationships. In addition to the particle exchange model, a neutral network was developed and used to identify data sufficiency throughout surface descriptor space, which will enable real-time feedback during future data production to ensure data is produced where it is most needed, and we provide commentary on improvements to the data production workflow for future endeavors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Temperature and density dependent pair potential for deuterium under shock

Large-scale classical molecular dynamics (CMD) simulations naturally include the microscopic physics necessary for atomistic modeling of shock release at the ablator-fuel interface in an inertial confinement fusion (ICF) capsule. Here, the multi-megabar shocks utilized in ICF experiments can drive the deuterium fuel from ambient to electron volt temperatures (T) and multi-fold compression. Modeling interatomic interactions over such an extreme range of conditions is challenging for empirical bond order potentials. We generate a pair potential for deuterium with explicit temperature and mass density dependence from ab initio density functional theory molecular dynamics using the iterative Boltzmann inversion method. This potential accurately reproduces the radial distribution functions and pressures from DFT in CMD equilibrium simulations across a wide range of thermodynamic conditions, yet fails to return the expected Hugoniot relations when used in direct CMD shock simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Finally, challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures

A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle (“all chemistry is local”), using information about the position of an atom’s neighbors to predict a per-atom energy. Here, in this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML’s nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force—that of the central atom—is added to the growing training set, preventing the noisy and irrelevant information from the piece’s boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

Abstract The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å −1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Science & Technology - Other Topics↗
DOE OSTI2020

Plasticity of irradiated materials at the nano and micro-scales

Here, we review here our recent work on plastic deformation in irradiated materials at the nano- and micro-scales, as revealed by Discrete Dislocation Dynamics (DDD) simulations. Two methods of including irradiation effects in the DDD framework are presented. The first directly captures the atomistic interaction mechanisms, while the second can effectively study high-dose irradiation. Computer simulations lead to new understanding of the dynamics of collective dislocation-irradiation defect interactions, as well as the quantitative analysis of the temporal and spatial characteristics associated with plastic instabilities. Based on these insights, theoretical models are developed to predict the critical conditions for dislocation channel formation. A simple probability model is proposed and demonstrated to predict the width of dislocation channels in bulk irradiated materials with good agreement with experimental data. The fundamental understanding of the origins of plastic flow localization in irradiated materials sheds light on the design of future generations of radiation-resistant materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stabilizing high-Ni cathodes with gradient surface Ti-enrichment

High-Ni cathodes are being intensely pursued worldwide for electric vehicles and other energy-dense applications due to their high capacity and low cost. However, structural instabilities during electrochemical cycling and when subjected to thermal treatment have been the major issues hindering their practical deployment. We here report a rational design of coating-integrated-into-synthesis protocol for fabricating surface Ti-enriched LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811#Ti) material. The coating to intermediates is crucial to obtain high structural ordering, both in the bulk and surface of high-Ni cathodes, and the Ti substitute has a unique tri-valence (Ti 3+ ) in a gradient surface distribution. Further, the simulations of projected density of states in the atomistic understanding further certify significantly enhanced stability of lattice oxygen for the NMC811 through such a Ti 3+ -based structure reinforcement. Consequently, the NMC811#Ti cathode delivers a high capacity up to 200mAhg -1 at 0.1 C, along with superior stabilities during air-storage and thermal treatment (up to 297°C at the fully charged state under differential scanning calorimetric measurements). The corresponding NMC811#Ti||graphite full cell exhibits a desired 83.6% capacity retention after 1000 cycles at 0.5 C in a voltage range of 2.8–4.3V. This work demonstrates a delicate surface reinforcement to stabilize high-Ni cathodes for long-life and safe lithium-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational Study of Indium Oxide Photoelectrodes

Using a combination of first-principles molecular dynamics simulations and electronic structure calculations, we characterize the atomistic structure and vibrational properties of a photocatalytic surface of In 2 O 3 , a promising photoelectrode for the production of hydrogen peroxide. We then investigate the surface in contact with water and show that the electronic states of In 2 O 3 are appropriately positioned in energy to facilitate the two-electron water oxidation reaction (WOR) over the competing four-electron oxygen evolution reaction. In conclusion, we further propose that the use of strained thin films interfaced with water is beneficial in decreasing the optical gap of In 2 O 3 and thus in utilizing a wider portion of the solar spectrum for the WOR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗