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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploring structural transitions at grain boundaries in Nb using a generalized embedded atom interatomic potential

The advancement in experimental techniques, like the atom probe tomography and high resolution electron microscopy, is fueling interest in studying structural transformations of grain boundaries in metal and alloys to uncover correlations between mechanical properties and solute or impurity segregation to grain boundaries. Atomistic modeling is an important tool that can pinpoint the intricate dynamics of grain boundary phase transitions, but the lack of accurate interatomic potentials needed to simulate the complex dynamics of grain boundary structural transitions and identify different metastable phases has been the bottleneck. To this end, we use niobium as a model body centered cubic (BCC) metal and develop an interatomic potential to study grain boundary phase transitions. The potential for Nb is based on a generalization of the embedded atomic method potential and has sufficient flexibility to learn complex energy landscapes using a small set of training structures. We systematically test and validate the using data from ab initio density functional theory calculations and experiments. Using this potential, we calculate energies of multiple symmetric-tilt grain boundaries spanning a wide range of misorientation angles. Additionally, we explore different metastable structures of the Σ 27(552) [$1\overline{1}0$] grain boundary and use molecular dynamic simulations to study the coexistence of metastable phases and grain boundary transitions at finite temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Analysis of phase stability and chemical segregation in the Mo-V alloys using a generalized embedded atom method potential

A new interatomic potential for the Mo-V system is introduced to facilitate the study of phase stability and mechanical properties at lower temperatures. This potential is based on a generalization of the embedded atom method and includes contributions from embedding energy, explicit two- and three-body interactions and nonlocal many-body interaction terms. The parameters of the potential are optimized by using data from ab initio density functional theory (DFT) calculations. The potential is rigorously validated across a range of physical properties, such as elastic constants, equation of states, phonon dispersion curves, point defect properties and melting temperatures for different compositions. Even though our potential is trained on a small dataset, its accuracy is comparable to available machine learning potentials for Mo and V. Furthermore, our results show that an ordered B2 phase is stable at low temperatures in alloys containing 50% V, but the solid solution phase is stable above 800 K. However, such long-range ordering is not observed in V-rich or Mo-rich alloys. In addition, our results show that V segregates to dislocation cores and grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mechanisms and stability of Li dynamics in amorphous Li-Ti-P-S-based mixed ionic–electronic conductors: A machine learning molecular dynamics study

Mixed ionic–electronic conductors (MIECs) exhibit both high ionic and electronic conductivity to improve the battery performance. In this work, we investigate the mechanism and stability of transport channels in our recently developed MIEC material, amorphous Ti-doped lithium phosphorus sulfide (LPS), using molecular dynamics (MD) simulations with a 99% accurate machine-learning force field (MLFF) trained on ab initio MD data. The achieved MLFF helps efficient large-scale MD simulations on LPS with three Ti concentrations (10%, 20%, and 30%) and six temperatures (25°C to 225°C) to calculate ionic conductivity, activation energy, Li-ion transport mechanism, and configurational entropy. Results show that ionic conductivities and activation energies are consistent with our recent experimental values. Moreover, Li-ion transport occurs via free-volume diffusion facilitated by the formation of disordered Li-S polyhedra. The enhanced stability of transport channels at 10% and 20% Ti doping, compared to 0% and 30%, is observed by analyzing the vibrational and configurational entropy of these disordered Li-S polyhedra. Overall, this study highlights the utility of MLFF-based large-scale MD simulations in explaining the transport mechanism and the stability of Li-ion in Ti-doped LPS electrolyte with significant computational efficiency.

And configuration entropy↗
DOE OSTI2024

Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained (CG) conformational dynamics. Constrained by the fluctuation–dissipation theorem, the approach can build CG models in dynamical consistency (DC) with all-atom molecular dynamics. Here, we also propose practical criteria for AIGLE to enforce long-term DC. Case studies of a toy polymer, with 20 CG sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling CG conformational dynamics in practice.

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

A Simple Correction for Nonadditive Dispersion within Extended Symmetry-Adapted Perturbation Theory (XSAPT)

Extended symmetry-adapted perturbation theory (XSAPT), in conjunction with empirical “+aiD” potentials fit to ab initio dispersion data, is a low-scaling approach to compute intermolecular interaction energies in noncovalent clusters. One shortcoming is that the aiD atom–atom dispersion potentials are independent of the chemical environment of the atoms in question and therefore neglect nonadditive dispersion effects. These can be significant in large systems, so to account for them we test a simple correction to XSAPT(KS)+aiD, where “KS” indicates the use of Kohn–Sham orbitals. This correction, which can be evaluated at fourth-order cost using double-ζ basis sets, is based on comparing second-order SAPT dispersion with and without a self-consistent charge embedding for the monomer wave functions. The correction amounts to ~1.4 kcal/mol in (H 2 O) 6 but ~5.5 kcal/mol in (H 2 O) 20 . With the nonadditive dispersion correction, XSAPT(KS)+aiD affords errors of ~1 kcal/mol for isomers of F–(H 2 O) 10 and (H 2 O) 20 , where the benchmarks are complete-basis CCSD(T) energies, as well as for ion–water clusters X(H 2 O)n where n ≤ 6 and X = F – , Cl – , SO 4 2– , Li + , Na + , or K + . We also test the MP2 method and a variety of density-functional methods that have been specifically recommended for noncovalent interactions. Among the latter, only ωB97X-V and ωB97M-V can be recommended for ion–water clusters, as mean errors for other popular approaches (including ωB97X-D3 and several Minnesota functionals) exceed 1 kcal/mol. Lastly, we examine clathrate-hydrate host/guest complexes whose mixture of hydrogen bonding and dispersion make them challenging tests for noncovalent quantum chemistry. Although the B97-D2 functional performs best for clathrate hydrates and has been previously recommended in other studies of these inclusion complexes, its performance for other systems examined here is quite poor. We are unable to find a functional whose accuracy is ≲1 kcal/mol accuracy for both clathrate hydrates and ion–water clusters. However, the XSAPT(KS)+aiD method with the nonadditive dispersion correction can achieve this, with a mean error for the clathrate hydrates of 0.3 kcal/mol.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Gas-Phase Formation of 1-Methylcyclopropene and 3-Methylcyclopropene via the Reaction of the Methylidyne Radical (CH; X 2 Π) with Propylene (CH 3 CHCH 2 ; X 1 A')

The crossed molecular beam reactions of the methylidyne radical (CH; X 2 Π) with propylene (CH 3 CHCH 2 ; X 1 A') along with (partially) substituted reactants were conducted at collision energies of 19.3 kJ mol –1 . Combining our experimental data with ab initio electronic structure and statistical calculations, the methylidyne radical is revealed to add barrierlessly to the carbon–carbon double bond of propylene reactant resulting in a cyclic doublet C 4 H 7 intermediate with a lifetime longer than its rotation period. These adducts undergo a nonstatistical unimolecular decomposition via atomic hydrogen loss through tight exit transition states forming the cyclic products 1-methylcyclopropene and 3-methylcyclopropene with overall reaction exoergicities of 168 ± 25 kJ mol –1 . These C 4 H 6 isomers are predicted to exist even in low-temperature environments such as cold molecular clouds like TMC-1, since the reaction is barrierless and exoergic, all transition states are below the energy of the separated reactants, and both the methylidyne radical (CH; X 2 Π) and propylene reactant were detected in cold molecular clouds such as TMC-1.

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

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

Here, we introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (β-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

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

Ephemeral collision complexes mediate chemically termolecular transformations that affect system chemistry [Ephemeral collision complexes induce chemically termolecular transformations that affect global chemistry]

Termolecular association reactions involve ephemeral collision complexes—formed from the collision of two molecules—that collide with a third and chemically inert ‘bath gas’ molecule that simply transfers energy to/from the complex. These collision complexes are generally not thought to react chemically on collision with a third molecule in the gas-phase systems of combustion and planetary atmospheres. Such ‘chemically termolecular’ reactions, in which all three molecules are involved in bond making and/or breaking, were hypothesized long ago in studies establishing radical chain branching mechanisms, but were later concluded to be unimportant. Here, with data from ab initio master equation and kinetic-transport simulations, we reveal that reactions of H+O 2 collision complexes with other radicals constitute major kinetic pathways under common combustion situations. These reactions are also found to influence flame propagation speeds, a common measure of global reactivity. As a result, analogous chemically termolecular reactions mediated by ephemeral collision complexes are probably of significance in various combustion and planetary environments.

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

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗
DOE OSTI2020

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2019

A combined experimental and computational study on the reaction dynamics of the 1-propynyl radical (CH 3 CC; X 2 A 1 ) with ethylene (H 2 CCH 2 ; X 1 A 1g ) and the formation of 1-penten-3-yne (CH 2 CHCCCH 3 ; X 1 A')

The crossed molecular beam reactions of the 1-propynyl radical (CH 3 CC; X 2 A 1 ) with ethylene (H 2 CCH 2 ; X 1 A 1g ) and ethylene-d4 (D 2 CCD 2 ; X 1 A 1g ) were performed at collision energies of 31 kJ mol ₋1 under single collision conditions. Combining our laboratory data with ab initio electronic structure and statistical Rice–Ramsperger–Kassel–Marcus (RRKM) calculations, we reveal in this work that the reaction is initiated by the barrierless addition of the 1-propynyl radical to the π-electron density of the unsaturated hydrocarbon of ethylene leading to a doublet C 5 H 7 intermediate(s) with a life time(s) longer than the rotation period(s). The reaction eventually produces 1-penten-3-yne ( p1 ) plus a hydrogen atom with an overall reaction exoergicity of 111 ± 16 kJ mol ₋1 . About 35% of p1 originates from the initial collision complex followed by C–H bond rupture via a tight exit transition state located 22 kJ mol ₋1 above the separated products. The collision complex ( i1 ) can also undergo a [1,2] hydrogen atom shift to the CH 3 CHCCCH 3 intermediate (i2) prior to a hydrogen atom release; RRKM calculations suggest that this pathway contributes to about 65% of p1 . In higher density environments such as in combustion flames and circumstellar envelopes of carbon stars close to the central star, 1-penten-3-yne ( p1 ) may eventually form the cyclopentadiene (c-C 5 H 6 ) isomer via hydrogen atom assisted isomerization followed by hydrogen abstraction to the cyclopentadienyl radical (c-C 5 H 5 ) as an important pathway to key precursors to polycyclic aromatic hydrocarbons (PAHs) and to carbonaceous nanoparticles.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

First-principles thermodynamic assessment of Sr-containing secondary phase formation in strontium-substituted lanthanum manganites for solid oxide cell applications

Sr-secondary phase formation is a potentially significant degradation mode with direct impact upon solid-oxide cell (SOC) commercial viability. A first-principles based thermodynamic study was performed for La 1−x Sr x MnO 3±δ (LSM) perovskites to assess their stability against formation of different Sr-secondary phases, including SrO, SrCrO 4 , SrSO 4 , SrCO 3 , and Sr(OH) 2 , for SOC applications. The Sr-secondary phase formation reaction free energies were determined via a thermodynamic model by combining ab initio lattice dynamics calculations for the solid phases and ab initio thermodynamic data for the gas phases. The current approach expands the previously reported thermodynamic modeling studies by integrating first-principles based point defect equilibria into the thermodynamic analysis. The modeling results obtained using this new approach indicate an increased tendency to form the SrO oxide upon decreasing the oxygen partial pressure. Additionally, the enhancing factors to form the Sr-related secondary phase from the associated SrO activity in LSM are further quantified by considering the equilibrium of SrO reacting with the contaminant gas species as a function of temperature and pressure.

defect thermodynamics modeling↗
DOE OSTI2026

NOVEL AQUEOUS GEOCHEMISTRY AT EXTREME CONDITIONS

The overall aim of the project is to develop quantitative predictive geochemical modelling of aqueous fluid-rock-hydrocarbon interactions in the Earth by building on recent state-of-the-art experimental data and ab initio calculations of extreme aqueous fluid chemistry

Source record↗
NASA NTRS1992

Equilibrium properties of transition-metal ion-argon clusters via simulated annealing

The geometrical structures of M(+) (Ar)n ions, with n = 1-14, have been studied by the minimization of a many-body potential surface with a simulated annealing procedure. The minimization method is justified for finite systems through the use of an information theory approach. It is carried out for eight potential-energy surfaces constructed with two- and three-body terms parametrized from experimental data and ab initio results. The potentials should be representative of clusters of argon atoms with first-row transition-metal monocations of varying size. The calculated geometries for M(+) = Co(+) and V(+) possess radial shells with small (ca. 4-8) first-shell coordination number. The inclusion of an ion-induced-dipole-ion-induced-dipole interaction between argon atoms raises the energy and generally lowers the symmetry of the cluster by promoting incomplete shell closure. Rotational constants as well as electric dipole and quadrupole moments are quoted for the Co(+) (Ar)n and V(+) (Ar)n predicted structures.

Asher, Robert L.↗
NASA NTRS1995

A Coupled-Cluster Study of the Molecular Structure, Vibrational Spectrum, and Heats of Formation of XONO2 (X=H, F, Cl)

The equilibrium structures, harmonic vibrational frequencies, dipole moments, and IR intensities of nitric acid, fluorine nitrate, and chlorine nitrate have been investigated by using the singles and doubles coupled cluster method that also includes a perturbational estimate of the effects of connected triple excitations, CCSD(T). A standard triple-zeta double-polarized basis set was utilized. The equilibrium geometries and vibrational spectra of HONO2 and ClONO2 are shown to be in excellent agreement with the available experimental data. The ab initio vibrational spectrum of FONO2 is also shown to be in excellent agreement with experiment. Unlike the FOOF and FON molecules, but similar to the cis- and trans-FONO molecules, FONO2 is shown to possess normal bond distances. The bonding in FONO2 is shown to be more similar to that in ClONO2 than that in HONO2, although there are still significant differences, especially in the partial atomic charges as deduced from Mulliken populations. This causes FONO2 to possess almost no dipole moment, which is very different to both HONO2 and ClONO2. By using large atomic natural orbital basis sets, CCSD(T) energies are computed for four isodesmic reactions in order to determine an accurate heat of formation for FONO2. Our best estimate for DELTA H(raised circle) (sub f,298) is 3.1 plus or minus 2.0 kcal/mol, indicating that the F-ONO2 bond energy is 31.3 kcal/mol.

Lee, Timothy J.↗
NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗