Search NASA⌕ Search

SEARCH · Search NASA

Results for “MOLECULAR DYNAMICS METHOD”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics

In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system’s specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. In this work, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics.

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

Controlling Structural, Electronic, and Energy Flow Dynamics of Catalytic Processes through Tailored Nanostructures

MoS 2 (molybdenum disulfide) is a highly-versatile catalyst material for support of numerous reactions from hydrodesulfurization and denitrogenation to the focus of this renewal proposal: hydrogenation of CO/CO 2 towards (higher) alcohols. At the same time, MoS 2 is a non-toxic, environmentally-benign and rather inert material- which under ambient conditions for some time has served as a lubricant and, more recently, as next-generation electronic material. The apparent contrast between inertness and stability in ambient, and catalytic activity under reactive conditions is puzzling and calls for a synergistic theoretical and experimental investigation with the long-term objective of enabling the rational design of MoS 2 -based catalysts for alcohol-formation reactions by providing a microscopic understanding of the environmental factors that determines site activity and selectivity. Our research project seeks answers to the questions (a) what conformation does MoS 2 adopt under reaction conditions (as opposed to that under ultrahigh vacuum and low temperatures)?; (b) what reaction pathways exist on such a material?; (c) how can the local environment of the active sites be manipulated so as to make MoS 2 an efficient catalyst for production of higher alcohol from syngas? In particular, research strategies will explore how the basal plane composed of sulfur atoms can be activated so as to exhibit a reactivity of its own, by addressing three research targets and building on extensive preliminary and enabling work: (1) vacancies and vacancy aggregates on the basal plane; (2) non-local catalyst transformation through alkali doping, hydrogenation and phase transition; (3) fabrication of a metal-nanoparticle-activated MoS 2 system, in which particle anchoring, reactive sites and pathways as well as selectivity are controlled by design, as an example of predictive development of a catalyst material,. All strategies are directed to improve the efficacy of the key reactive sites and selectivity of chemical pathways by design, to replace the inefficient methodology of trial and error in catalyst development. This research project represents a synergistic combination of computational guidance, foundational surface-science-based experiments and validation under reactive conditions that aims at transformative new insights into the working of MoS 2 -based hydrogenation-catalysts, a topic squarely at the center of the interest of DOE BES. Guided and led by Talat Rahman, a computational physicist, this project will apply density functional theory to understand structure, reaction pathways and chemical potentials associated with MoS 2 -based CO/CO 2 hydrogenation, augmented by kinetic Monte Carlo methods for reaction rates and prefactors as well as ab-initio molecular dynamics for evaluation of thermal stability. Complementary experimental input and validation will originate from co-PI Ludwig Bartels, a physical chemist and materials scientist, whose group focuses on local imaging and preparation of MoS 2 materials, from co-PI Peter Dowben, an experimental physicist, whose group is expert in the spectroscopy of occupied and unoccupied electronic states, and from senior collaborator Michael White of Brookhaven National Laboratory, whose group generates high-resolution electronic and activity information on size-selected well-defined metal chalcogenide clusters. This collaborative effort will enable a comprehensive understanding of the correlation of structural integrity and catalytic activity of MoS 2 in forms ranging from extended films to individual particles with known geometries and binding sites. Alcohol formation from syngas is a rapidly emerging application that has great potential through facile, economic, and decentralized biomass gasification. CO 2 activation is one of the most pressing concerns of our time: increasing CO 2 levels in the atmosphere change the climate and expose the globe to environmental transformations with the potential for enormous economic and societal impact. We will investigate CO/CO 2 hydrogenation via an interdisciplinary research collaboration with established synergy – one that, in accordance with the mission of the DOE, involves accredited Hispanic-Serving Institutions and that, through student exchange with international collaborators and National Labs, will directly benefit a broad spectrum of communities and generate human resources in sciences essential to their future.

2D materials↗
DOE OSTI2022

Benchmarking structural evolution methods for training of machine learned interatomic potentials

When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. Here, we benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional theory training data sets for MLIPs. We also discuss the generation of initial structures which are either from known structures or from random structures in detail to further formalize the structure-sourcing processes in the future. The polymorph-rich zirconium-oxygen composition space is used as a rigorous benchmark system for comparing the performance of MLIPs trained on structures generated from these structural evolution methods. Using Behler–Parrinello neural networks as our MLIP models, we find that CE and the DM searches are generally superior to MD in terms of spatial descriptor diversity and statistical accuracy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Fast and accurate calculation of EXAFS Debye-Waller factors in U⁢O2 using the dynamical matrix method

Theoretical modeling of bonding dynamics in metal oxides is required for predicting their thermal conductivity, catalytic activity, and mechanical properties. A primary challenge is the scarcity of experimental methods for validating theoretical predictions of these atomic-scale dynamics. This work presents a workflow that uses experimental extended x-ray absorption fine structure (EXAFS) data collected at high temperatures to validate an interatomic force field for uranium dioxide (UO2), an important model material. The validated force field is then used to drive computationally intensive molecular dynamics (MD) simulations and as input for the much faster dynamical matrix Debye-Waller (DMDW) method. The predicted values of the Debye-Waller factors from the DMDW calculations are in good agreement with those obtained from the MD simulations, with residual pair-specific differences attributable to quantum zero-point motion at low temperatures and lattice anharmonicity at high temperatures. We further show that theoretical EXAFS spectra constructed directly from DMDW-derived Debye-Waller factors reproduce the experimental data (at relatively low temperatures) with accuracy comparable to full MD-EXAFS, providing an additional validation of the choice of the potential. This study establishes a validated, rapid computational pathway for modeling bond dynamics, naturally incorporating quantum nuclear\\\\r\\\\nstatistics absent in classical simulations, which are essential for the mechanistic understanding of complex oxide materials.

58 GEOSCIENCES↗
DOE OSTI2022

Ab Initio Phase Diagram of Chromium to 2.5 TPa

Chromium possesses remarkable physical properties such as hardness and corrosion resistance. Chromium is also a very important geophysical material as it is assumed that lighter Cr isotopes were dissolved in the Earth’s molten core during the planet’s formation, which makes Cr one of the main constituents of the Earth’s core. Unfortunately, Cr has remained one of the least studied 3d transition metals. In a very recent combined experimental and theoretical study (Anzellini et al., Scientific Reports, 2022), the equation of state and melting curve of chromium were studied to 150 GPa, and it was determined that the ambient body-centered cubic (bcc) phase of crystalline Cr remains stable in the whole pressure range considered. However, the importance of the knowledge of the physical properties of Cr, specifically its phase diagram, necessitates further study of Cr to higher pressure. In this work, using a suite of ab initio quantum molecular dynamics (QMD) simulations based on the Z methodology which combines both direct Z method for the simulation of melting curves and inverse Z method for the calculation of solid–solid phase transition boundaries, we obtain the theoretical phase diagram of Cr to 2.5 TPa. We calculate the melting curves of the two solid phases that are present on its phase diagram, namely, the lower-pressure bcc and the higher-pressure hexagonal close-packed (hcp) ones, and obtain the equation for the bcc-hcp solid–solid phase transition boundary. We also obtain the thermal equations of state of both bcc-Cr and hcp-Cr, which are in excellent agreement with both experimental data and QMD simulations. We argue that 2180 K as the value of the ambient melting point of Cr which is offered by several public web resources (“Wikipedia,” “WebElements,” “It’s Elemental,” etc.) is most likely incorrect and should be replaced with 2135 K, found in most experimental studies as well as in the present theoretical work.

equation of state↗
DOE OSTI2020

Molecular dynamics study on interface formation and bond strength of impact-welded Mg-steel joints

It was recently demonstrated that the vaporizing foil actuator welding (VFAW) method can directly join immiscible magnesium and steel alloys without coating or a third chemical element based intermetallic compound layer. The VFAW Mg/steel joint exhibits a mixed interface layer of up to 200μm thickness consisting of Mg matrix and Fe particles. Computer simulations have suggested the formation of the interlayer is from the high-velocity frictional shearing between the Mg/steel substrates during the oblique impact in the VFAW process. This paper investigates the formation of Mg-Fe interlayer under VFAW condition with different shearing velocities using molecular dynamics (MD) model, and studies the bonding strength under different scenarios. Finally, the results elucidate the critical role of shearing velocity and surface roughness in achieving Mg/Fe joint.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Excitons: Energetics and spatiotemporal dynamics

The concept of an exciton as a quasiparticle that represents collective excited states was originally adapted from solid-state physics and has been successfully applied to molecular aggregates by relying on the well-established limits of the Wannier exciton and the Frenkel exciton. However, the study of excitons in more complex chemical systems and solid materials over the past two decades has made it clear that simple concepts based on Wannier or Frenkel excitons are not sufficient to describe detailed excitonic behavior, especially in nano-structured solid materials, multichromophoric macromolecules, and complex molecular aggregates. In addition, important effects such as vibronic coupling, the influence of charge-transfer (CT) components, spin-state interconversion, and electronic correlation, which had long been studied but not fully understood, have turned out to play a central role in many systems. This has motivated new experimental approaches and theoretical studies of increasing sophistication. This article provides an overview of works addressing these issues that were published for A Special Topic of the Journal of Chemical Physics on ``Excitons: Energetics and spatio-temporal dynamics" and discusses their implications.

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

Modeling Protein–Protein and Protein–Ligand Interactions by the ClusPro Team in CASP16

ABSTRACT In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein–protein and protein–ligand complex structures. For protein–protein docking, we combined physics‐based sampling—using ClusPro FFT docking and molecular dynamics—with AlphaFold (AF)‐based sampling, followed by AF‐based refinement. Our method produced numerous high‐accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics‐based sampling alongside deep learning‐based refinement. For protein–ligand docking, we integrated the ClusPro LigTBM template‐based approach with a machine learning‐based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics‐based sampling and a diffusion model. Our template‐based strategy achieved a mean lDDT‐PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics‐based modeling with AI‐driven refinement can significantly enhance the accuracy of both protein–protein and protein–ligand structure predictions.

Ashizawa, Ryota [Department of Applied Mathematics↗
DOE OSTI2024

The Challenge of Characterizing High-Concentration Electrolytes at the Molecular Level: A Perspective

High-concentration electrolytes (HCEs) are promising materials composed of highly concentrated salt solutions in organic solvents. HCEs have many desirable properties and are particularly important in the field of batteries. However, the number of ways in which these materials can be tuned is very large, which is crucial for tailored electrolyte design. Moreover, the molecular characterization of HCEs is challenging both experimentally and computationally, but it is necessary for their rational design. Therefore, currently the structure–property–performance relationship of these electrolytes has not been directly derived from their characterization. Here, in this Perspective, we present a brief overview of the HCEs and discuss the state-of-the-art characterization methods used to study them at the molecular level. We also address the challenges associated with these methods, including both experimental techniques and computational tools currently available. Emphasis is placed on methods aimed at understanding the physical phenomena that govern the molecular structure and dynamics occurring on the subnanosecond and nanometer time and length scales. Finally, we discuss new strategies for obtaining a comprehensive characterization of HCEs at the molecular level.

electrolytes↗
DOE OSTI2024

Unusual dynamics of tetrahedral liquids caused by the competition between dynamic heterogeneity and structural heterogeneity

Tetrahedral liquids exhibit intriguing thermodynamic and transport properties because of the various ways tetrahedra can be packed and connected. Recently, an unusual temperature dependence of the stretching exponent β in a model tetrahedral liquid ZnCl 2 from T m + 85 K to T m + 35 K has been reported using neutron-spin echo spectroscopy. This discovery stands in sharp contrast to other glass-forming liquids. In this study, we conducted neural network force field driven molecular dynamic simulations of ZnCl 2 . We found a non-monotonic temperature dependence of β from liquid to supercooled liquid temperatures. Further structural decomposition and dynamic analysis suggest that this unusual dynamic behavior is a result of the competition between the decrease in the diversity of tetrahedra motifs (structural heterogeneity) and the increase in glassy dynamic heterogeneity. Furthermore, this result may contribute to new understandings of the structural relaxation of other network liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Intermediate time sub-diffusion and stress relaxation in ring polymer melts

The slow dynamics of non-concatenated ring melts remains a frontier problem in polymer science with implications for many soft material environments including cellular biophysics. Here, in this work, we report large-scale simulations of model ring melts that analyze the monomer and center-of-mass (CM) mean square displacements (MSD) and stress relaxation function on intermediate time and length scales. The degree of dynamical slowing down is characterized by the maximally sub-diffusive fractional time scaling exponents. The data span an exceptionally wide range of ring degrees of polymerization and stiffnesses and are not successfully organized based on the classic measure linear chain entanglement, N/N e . Rather, we find that the crossover degree of polymerization, N D , based on ring macromolecular caging that successfully allows master curves to be constructed for the long-time CM self-diffusion constant also collapses these temporal dynamic scaling exponents. Different properties display different exponents and exhibit one or two regimes of linear variation with the logarithm of N D / N . A distinct crossover of the CM-MSD and stress relaxation exponents emerges at sufficiently large N or stiffness that is not found for the monomer MSD, indicating a novel form of dynamic decoupling. This crossover aligns with the predicted critical degree of polymerization for transitioning from a weak to strong caging regime, indicative of activated transport. The latter may reflect the emergence of an intermolecular collective contribution to stress in analogy with dense soft colloidal matter. Suggestions are made for future theoretical work to address the rich patterns of behavior discovered.

Anomalous diffusion↗
DOE OSTI2024

Dynamic population balance in molecular-level simulations of hypersonic flows

This report summarizes the work towards developing stochastic weighted particle methods (SWPM) for future application in hypersonic flows. Extensive changes to Sandia’s direct simulation Monte Carlo (DSMC) solver, SPARTA (Stochastic Particle Real Time Analyzer), were made to enable the necessary particle splitting and reduction capabilities for SWPM. The results from one-dimensional Couette and Fourier flows suggest that SWPM can reproduce the correct transport for a large range of Knudsen numbers with adequate accuracy. The associated velocity and temperature profiles are in good agreement with DSMC. An issue with particle placement during particle number reduction, is identified, to which, a simple but effective solution based on minimizing the center of mass error is proposed. High Mach wheel flows are simulated using the SWPM and DSMC methods. SWPM is capable of providing nearly an order of magnitude increase in efficiency over DSMC while retaining high accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2009

The Development of Directional Decohesion Finite Elements for Multiscale Failure Analysis of Metallic Polycrystals

Atomistic simulations of intergranular fracture have indicated that grain-scale crack growth in polycrystalline metals can be direction dependent. At these material length scales, the atomic environment greatly influences the nature of intergranular crack propagation, through either brittle or ductile mechanisms, that are a function of adjacent grain orientation and direction of crack propagation. Methods have been developed to obtain cohesive zone models (CZM) directly from molecular dynamics simulations. These CZMs may be incorporated into decohesion finite element formulations to simulate fracture at larger length scales. A new directional decohesion element is presented that calculates the direction of Mode I opening and incorporates a material criterion for dislocation emission based on the local crystallographic environment to automatically select the CZM that best represents crack growth. The simulation of fracture in 2-D and 3-D aluminum polycrystals is used to illustrate the effect of parameterized CZMs and the effectiveness of directional decohesion finite elements.

Saether, Erik↗
DOE OSTI2022

Extracting Inelastic Scattering Cross Sections for Finite and Aperiodic Materials from Electronic Dynamics Simulations

Explicit time-dependent electronic structure theory methods are increasingly prevalent in the areas of condensed matter physics and quantum chemistry, with the broad-band optical absorptivity of molecular and small condensed-phase systems nowadays routinely studied with such approaches. Here, in this paper, it is demonstrated that electronic dynamics simulations can similarly be employed to study cross sections for the scattering-induced electronic excitations probed in nonresonant inelastic X-ray scattering and momentum-resolved electron energy loss spectroscopies. A method is put forth for evaluating the electronic dynamic structure factor, which involves the application of a momentum boost-type perturbation and transformation of the resulting reciprocal space density fluctuations into the frequency domain. Good agreement is first demonstrated between the dynamic structure factor extracted from these electronic dynamics simulations and the corresponding transition matrix elements from linear response theory. The method is then applied to some extended (quasi)one-dimensional systems, for which the wave vector becomes a good quantum number in the thermodynamic limit. Finally, the dispersion of many-body excitations in a series of hydrogen-terminated graphene flakes (and twisted bilayers thereof) is investigated to highlight the utility of the presented approach for capturing morphology-dependent effects in the inelastic scattering cross sections of nanostructured and/or noncrystalline materials.

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

Real-space formulation of the stress tensor for O (N) density functional theory: Application to high temperature calculations

We present an accurate and efficient real-space formulation of the Hellmann–Feynman stress tensor for O (N) Kohn–Sham density functional theory (DFT). While applicable at any temperature, the formulation is most efficient at high temperature where the Fermi–Dirac distribution becomes smoother and the density matrix becomes correspondingly more localized. We first rewrite the orbital-dependent stress tensor for real-space DFT in terms of the density matrix, thereby making it amenable to O (N) methods. We then describe its evaluation within the O (N) infinite-cell Clenshaw–Curtis Spectral Quadrature (SQ) method, a technique that is applicable to metallic and insulating systems, is highly parallelizable, becomes increasingly efficient with increasing temperature, and provides results corresponding to the infinite crystal without the need of Brillouin zone integration. We demonstrate systematic convergence of the resulting formulation with respect to SQ parameters to exact diagonalization results and show convergence with respect to mesh size to the established plane wave results. Here, we employ the new formulation to compute the viscosity of hydrogen at 10 6 K from Kohn–Sham quantum molecular dynamics, where we find agreement with previous more approximate orbital-free density functional methods.

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

Dynamics of collision-induced energy transfer

Collisions are of fundamental importance in many relevant physico-chemical phenomena in the gas phase and at the gas-surface interface. Collision-induced energy transfer not only offers a sensitive probe of interatomic interactions, but also impacts an array of processes ranging from transport to reactivity. This perspective highlights some recent advances on collision-induced energy transfer dynamics. In the gas phase, cold collisions revealed remarkable quantum effects and stereodynamics of bimolecular inelastic scattering, which motivated development of new state-to-state quantum scattering methods. For surface processes, experiments of hydrogen atom scattering from various surfaces combined with first-principles molecular dynamics simulations shed valuable light on the relative importance of adiabatic energy transfer to surface phonons and nonadiabatic energy transfer to surface electrons. We outline further challenges in these fields and expect more fruitful interplay between experiment and theory.

Cold collision↗
DOE OSTI2022

Path sampling of recurrent neural networks by incorporating known physics

Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections.

59 BASIC BIOLOGICAL SCIENCES↗