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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep learning of accurate force field of ferroelectric HfO 2

The discovery of ferroelectricity in HfO 2 -based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density nonvolatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials are often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network–based interatomic force field of HfO 2 learned from ab initio data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. Here, the validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of HfO 2 with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of HfO 2 to related material systems including dopants and defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Uncertainty in master-equation-based rate coefficient calculations

Rate coefficients are key quantities in gas phase kinetics and can be determined theoretically via master equation (ME) calculations. Rate coefficients characterize how fast a certain chemical species reacts away due to collisions into a specific product. Some of these collisions are simply transferring energy between the colliding partners, in which case the initial chemical species can undergo a unimolecular reaction: dissociation or isomerization. Other collisions are reactive, and the colliding partners either exchange atoms, these are direct reactions, or form complexes that can themselves react further or get stabilized by deactivating collisions with a bath gas. The input of MEs are molecular parameters: geometries, energies, and frequencies determined from ab initio calculations. While the calculation of these rate coefficients using ab initio data is becoming routine in many cases, the determination of the uncertainties of the rate coefficients are often ignored, sometimes crudely assessed by varying independently just a few of the numerous parameters, and only occasionally studied in detail. In this study, molecular frequencies, barrier heights, well depths, and imaginary frequencies (needed to calculate quantum mechanical tunneling) were automatically perturbed in an uncorrelated fashion. Our Python tool, MEUQ, takes user requests to change all or specified well, barrier, or bimolecular product parameters for a reaction. We propagate the uncertainty in these input parameters and perform global sensitivity analysis in the rate coefficients for the ethyl + O 2 system using state-of-the-art uncertainty quantification (UQ) techniques via Python interface to UQ Toolkit (www . sandia.gov/uqtoolkit). A total of 10,000 sets of rate coefficients were collected after perturbing 240 molecular parameters. With our methodology, sensitive mechanistic steps can be revealed to a modeler in a straightforward manner for identification of significant and negligible influences in bimolecular reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Uncertainty in master-equation-based rate coefficient calculations

Rate coefficients are key quantities in gas phase kinetics and can be determined theoretically via master equation (ME) calculations. Rate coefficients characterize how fast a certain chemical species reacts away due to collisions into a specific product. Some of these collisions are simply transferring energy between the colliding partners, in which case the initial chemical species can undergo a unimolecular reaction: dissociation or isomerization. Other collisions are reactive, and the colliding partners either exchange atoms, these are direct reactions, or form complexes that can themselves react further or get stabilized by deactivating collisions with a bath gas. The input of MEs are molecular parameters: geometries, energies, and frequencies determined from ab initio calculations. While the calculation of these rate coefficients using ab initio data is becoming routine in many cases, the determination of the uncertainties of the rate coefficients are often ignored, sometimes crudely assessed by varying independently just a few of the numerous parameters, and only occasionally studied in detail. In this study, molecular frequencies, barrier heights, well depths, and imaginary frequencies (needed to calculate quantum mechanical tunneling) were automatically perturbed in an uncorrelated fashion. Our Python tool, MEUQ, takes user requests to change all or specified well, barrier, or bimolecular product parameters for a reaction. We propagate the uncertainty in these input parameters and perform global sensitivity analysis in the rate coefficients for the ethyl + O 2 system using state-of-the-art uncertainty quantification (UQ) techniques via Python interface to UQ Toolkit (www.sandia.gov/uqtoolkit). A total of 10,000 sets of rate coefficients were collected after perturbing 240 molecular parameters. With our methodology, sensitive mechanistic steps can be revealed to a modeler in a straightforward manner for identification of significant and negligible influences in bimolecular reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Planetary Ices and the Linear Mixing Approximation

Here, the validity of the widely used linear mixing approximation (LMA) for the equations of state (EOSs) of planetary ices is investigated at pressure–temperature conditions typical for the interiors of Uranus and Neptune. The basis of this study is ab initio data ranging up to 1000 GPa and 20,000 K, calculated via density functional theory molecular dynamics simulations. In particular, we determine a new EOS for methane and EOS data for the 1:1 binary mixtures of methane, ammonia, and water, as well as their 2:1:4 ternary mixture. Additionally, the self-diffusion coefficients in the ternary mixture are calculated along three different Uranus interior profiles and compared to the values of the pure compounds. We find that deviations of the LMA from the results of the real mixture are generally small; for the thermal EOSs they amount to 4% or less. The diffusion coefficients in the mixture agree with those of the pure compounds within 20% or better. Finally, a new adiabatic model of Uranus with an inner layer of almost pure ices is developed. The model is consistent with the gravity field data and results in a rather cold interior (${T}_{\mathrm{core}}\sim 4000$ K).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Semi-Empirical Interatomic Potential for Large-Scale Molecular Dynamics Simulation of Metal-Oxide Systems

Classical molecular dynamics (MD) simulation can be applied to systems containing billions of atoms during times up to microseconds. However, utilization of a large-scale MD simulation requires reliable but computationally cheap interatomic potentials. In the case of metallic systems, embedded atom method (EAM) and Finnis-Sinclair (FS) potentials are the reasonable choices, but their development for multicomponent alloys is a challenge. Addition of oxygen atoms makes the problem of developing semi-empirical potentials even more difficult because the long-range Coulomb interaction cannot be neglected, and different atoms have different charges which vary during MD relaxation such that a charge equilibration procedure should be applied every MD step. As results researchers tend to avoid simulating metal-oxide systems. In this talk, a new Finnis-Sinclair (FS) Ni-Co-Cr potential will be presented. Special attention will be paid to reproducing of the element melting temperatures and clustering. Next, a charge transfer ionic potential (CTIP) developed to simulate the NiO properties will be presented. This potential utilizes the Ni FS potential and accounts for the Coulomb interaction in the vicinity of oxygen atoms. Fitting of the CTIP parameters and FS potential functions describing the interaction with oxygen atoms to the available experimental and ab initio data will be discussed. Results of MD simulation of interaction of NiO particles with dislocations in Ni will be shown.

molecular dynamics simulation↗
DOE OSTI2017

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen

New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater- Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value of an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.

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

Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1 0 FeNi."

We describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its dependence on the magnetic state, and then proceed to describe the consequent MCA, magnetisation, and magnetic critical temperature (Curie temperature). Crucially, within our modelling framework, the same ab initio description of a material’s electronic structure determines all aspects. We demonstrate this holistic method by studying the effects of alloying additions in FeNi, examining systems with the general stoichiometries Fe 4 Ni 3 X and Fe 3 Ni 4 X, for additives including X = Pt, Pd, Al, and Co. The atomic ordering behaviour predicted on adding these elements, fundamental for determining a material’s MCA, is rich and varied. Equiatomic FeNi has been reported to require ferromagnetic order to establish the tetragonal L1 0 order suited for significant MCA. Our results show that when alloying additions are included in this material, annealing in an applied magnetic field and/or below a material’s Curie temperature may also promote tetragonal order, along with an appreciable effect on the predicted hard magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development of Multicomponent EAM Potential for Ni-Based Superalloy

We initiated the development of multi-component EAM potentials for Aluminides and Carbides, key phases in Ni-based Superalloys. The goal is to utilize the MD simulation to understand the deformation dynamics that contribute to the formation of voids and creep initiation. For this purpose, we constructed the raw data from ab-initio (molecular dynamics) MD simulations fed into the potential development code and used Nickel as the base metal with the addition of a number of various elements including Aluminum, Chromium, Tungsten. We then developed the EAM potentials for the aluminide and carbide phases using the force-fitting code MEAMfit. Our generated potential reproduces the fundamental properties of the Ni3Al and Mo23C6 phases. We verified further the EAM potential through the thermal stability test at different temperatures and by reproducing the elastic constants consistent with the experimental values.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Application of an ab initio S matrix to data analysis of transfer reactions to the continuum populating Be 11

Recently, the bound and continuum spectrum of 11 Be has been calculated within the ab initio no-core shell model with continuum (NCSMC) method, with the parity inversion in the ground state successfully reproduced. Here, the continuum spectrum obtained is in agreement with known experimental levels. The S matrix contained in the NCSMC continuum wave functions of the n + 10 Be system is used in this work for the first time in a transfer-to-the-continuum (TC) reaction calculation. The TC approach is applied to study the excitation energy spectrum of 11 Be measured in the 9 Be( 18 O, 16 O) 11 Be reaction at 84 MeV. Previously known levels are confirmed and theoretical and experimental evidence for a 9/2 + state at E x = 5.8 MeV is given, whose configuration is thought to be 10 Be(2 + )+n( d 5/2 ).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

BEAST DB: Grand-Canonical Database of Electrocatalyst Properties

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze the electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain a granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage.

database↗
DOE OSTI2024

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. Further, the accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

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

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Fast event-based electron counting for small-molecule structure determination by MicroED

Electron counting helped realize the resolution revolution in single-particle cryoEM and is now accelerating the determination of MicroED structures. Its advantages are best demonstrated by new direct electron detectors capable of fast (kilohertz) event-based electron counting (EBEC). This strategy minimizes the inaccuracies introduced by coincidence loss (CL) and promises rapid determination of accurate structures. We used the Direct Electron Apollo camera to leverage EBEC technology for MicroED data collection. Given its ability to count single electrons, the Apollo collects high-quality MicroED data from organic small-molecule crystals illuminated with incident electron beam flux densities as low as 0.01–0.045 e − /Å 2 /s. Under even the lowest flux density (0.01 e − /Å 2 /s) condition, fast EBEC data produced ab initio structures of a salen ligand (268 Da) and biotin (244 Da). Each structure was determined from a 100° wedge of data collected from a single crystal in as few as 50 s, with a delivered fluence of only ∼0.5 e − /Å 2 . Fast EBEC data collected with a fluence of 2.25 or 3.33 e − /Å 2 also facilitated a 1.5 Å structure of thiostrepton (1665 Da). While refinement of these structures appeared unaffected by CL, a CL adjustment applied to EBEC data further improved the distribution of intensities measured from the salen ligand and biotin crystals. However, CL adjustment only marginally improved the refinement of their corresponding structures, signaling the already high counting accuracy of detectors with counting rates in the kilohertz range. Overall, by delivering low-dose structure-worthy data, fast EBEC collection strategies open new possibilities for high-throughput MicroED.

EBEC↗
DOE OSTI2023

Development of an interatomic potential for the W–Ta system

A physics-inspired, data-driven interatomic potential framework for multi-element system is presented. This potential is based on the generalization of the embedded atom method potential and systematically incorporates two-, three- and many-body effects. Different atomic environments are described by atom-centered Gaussian basis sets which provides sufficient flexibility to capture diverse atomic environments and allows for easy optimization of the free parameters. An interatomic potential model for the tungsten–tantalum (W–Ta) system is developed using this framework by training on data from ab initio density functional theory (DFT) calculations. The potential is thoroughly tested at various compositions by comparing bulk and defect properties from experimental and DFT data. It is shown that the potential predicts the elastic constants, defect properties, such as vacancy and interstitial formation energies, core structures of dislocations, and melting points for both pure and tantalum and tungsten with an accuracy comparable to other single element machine learning based potentials. In conclusion, the potential model is used to investigate the formation energies of ordered alloys and vacancy formation energies for chemically random W–Ta alloys.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Ab Initio and Monte Carlo Approaches for the Magnetocaloric Effect in BaMnO{sub 3} Oxide Perovskite

The self-consistent ab initio calculations, based on density functional theory approach (DFT) and using full potential linearised augmented plane wave (FLAPW) method, have been used to investigate both electronic and magnetic properties of the BaMnO{sub 3} perovskite. Spin-polarised calculations, including the spin-orbit interaction, are used to determine the energy of the ferromagnetic (FM) and antiferromagnetic (AFM) states of BaMnO{sub 3} perovskite. Obtained data from ab initio calculations are used as input for the Monte Carlo simulations to compute other magnetic parameters. Magnetisation, specific heat and magnetic entropy change have been given using the Monte Carlo simulations. The adiabatic temperature change, transition temperature and relative cooling power have been established.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Gas–Phase Synthesis of 3–Vinylcyclopropene via the Crossed Beam Reaction of the Methylidyne Radical (CH; X 2 Π) with 1,3–Butadiene (CH 2 CHCHCH 2 ; X 1 Ag)

The crossed molecular beam reactions of the methylidyne radical (CH; X 2 Π) with 1,3-butadiene (CH 2 CHCHCH 2 ; X 1 Ag) along with their (partially) deuterated counterparts were performed at collision energies of 20.8 kJ mol -1 under single collision conditions. Combining our laboratory data with ab initio calculations, we reveal that the methylidyne radical may add barrierlessly to the terminal carbon atom and/or carbon-carbon double bond of 1,3-butadiene, leading to doublet C 5 H 7 intermediates with life times longer than the rotation periods. Furthermore, these collision complexes undergo non-statistical unimolecular decomposition through hydrogen atom emission yielding the cyclic cis- and trans-3-vinyl-cyclopropene products with reaction exoergicities of 119±42 kJ mol -1 . Since this reaction is barrierless, exoergic, and all transition states are located below the energy of the separated reactants, these cyclic C 5 H 6 products are predicted to be accessed even in low-temperature environments, such as in hydrocarbon-rich atmospheres of planets and cold molecular clouds such as TMC-1.

3-vinylcyclopropene↗
DOE OSTI2018

First Full Structural Characterization of Chloro Formamidinium Salts

While the synthesis of chloro formamidinium chloride has been known for over one century, no solid‐state structure of a chlorine‐substituted formamidinium compound has been reported up to now. Herein, we describe syntheses, solid‐state structures, and vibrational properties of four chloro formamidinium compounds, namely [ClC(NH 2 ) 2 ]Cl ( 1 ), [ClC(NH 2 ) 2 ](AuCl 4 ) ( 2 ), [ClC(NH 2 ) 2 ] 2 (PdCl 4 ) ( 3 ), and [ClC(NH 2 ) 2 ] 2 (PtCl 6 ) ( 4 ), as determined by single‐crystal X‐ray diffraction (SCXRD). 1 and 3 crystallize in the monoclinic space group P 2 1 / c , 2 in the orthorhombic space group Pbcn , whereas 4 adopts the triclinic space group P 1 . By using a combination of high‐resolution time‐of‐flight neutron powder diffraction, state‐of‐the‐art DFT calculations as well as IR spectroscopy, the prior SCXRD result was fully confirmed and augmented by determining the hydrogen‐bonded molecular networks. First‐principles electronic‐structure calculations corroborate the ionic nature of these salts by showing very flat, molecular‐like bands next to the Fermi level, in addition to indirect bandgaps. This study underlines the upmost importance of complementing diffraction data by ab initio calculations, as theory‐derived anisotropic displacement parameters served as fixed numerical entries.

Möller, Andreas↗