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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Machine Learning a Simple Interpretable Short-Range Potential for Silica

A wide array of models, spanning from computationally expensive ab initio methods to a spectrum of force-field approaches, have been developed and employed to probe silica polymorphs and understand growth processes and atomic-level dynamical transitions in silica. However, the quest for a model capable of making accurate predictions with high computational efficiency for various silica polymorphs is still ongoing. Recent developments in short-range machine-learned models, such as GAP and NNPScan, have shown promise in providing reasonable descriptions of silica, but their computational cost remains high compared to force fields such as BKS which are based on simple interpretable functional forms. Here, in this study, we build on the recent success of our reinforcement learning (RL) workflow to derive a new set of optimal parameters for a promising short-range BKS-based model proposed by Soules. We use RL to navigate the eight-dimensional parameter space of the Soules potential using an experimental training data set that includes both local and global structural features from approximately 21 experimentally realized silica polymorphs, including high density phases and porous zeolites. We compare the performance of our machine-learned ML-Soules model with other high quality models including our recent machine-learned parametrization of BKS (ML-BKS), a machine-learned potential (GAP), as well as predictions of ab initio calculations with the highly fidelity SCAN functional. The ML-Soules accurately captures the relative energetic ordering of various polymorphs as well as their structural features at a significantly reduced computational expense. The ML-Soules model also reasonably captures the structure, density, and elastic constants of quartz, as well as metastable silica polymorphs. We further discuss the limitations of the Soules functional form and propose potential enhancements, including the incorporation of additional three-body terms and/or the utilization of different short-ranged functional forms to achieve greater accuracy for both global and local features in the modeling of silica while retaining low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

Ab initio study of the alkali and alkaline-earth monohydroxides

A systematic study of the structures and dissociation energies of all the alkali and alkaline-earth monohydroxides is conducted. A theoretical model for determining accurate dissociation energies of ionic molecules is discussed. The obtained theoretical structures and dissociation energies of the alkali and alkaline-earth monohydroxides, respectively, are compared with experimental data. It is found that the theoretical studies of the bending potentials of BeOH, MgOH, and CaOH reveal the different admixture of covalent character in these systems. The BeOH molecule with the largest degree of covalent character is found to be bent (theta equals 147 deg). The MgOH is also linear. The theoretical dissociation energies for the alkali and akaline-earth hydroxides are thought to be accurate to 0.1 eV.

Bauschlicher, C. W., Jr.↗
DOE OSTI2021

First principles molecular dynamics maps out complete mineral surface acidity landscape

In “Interfacial structures and acidity constants (pKa) of goethite from first principles molecular dynamics simulations,” authors Y. Zhang, X. Lui, J. Cheng, and X. Lu apply First Principles molecular dynamics (FPMD, also called Density Functional Theory MD, DFT/MD, or ab initio MD, AIMD), to evaluable the complete set of acidity constants (pK a ) of the hydroxyl groups on the most prominent goethite crystal facets. The pK a of these OH and OH$^+_2$ groups are compared with available data from the multisite complexation (MUSIC) model traditionally used to estimate pK a on mineral surfaces. The authors have presented eloquent rationale for the importance and implications of understanding goethite acidity constants in room temperature geochemistry settings. In this paper, I focus on the computational aspects, the strengths of FPMD, and its possibilities.

58 GEOSCIENCES↗
NASA NTRS2021

What it Takes to Compute Highly Accurate Rovibrational Line Lists for Use in Astrochemistry

We review the Best Theory + Reliable High-resolution Experiment (BTRHE) strategy for obtaining highly accurate molecular rovibrational line lists with InfraRed (IR) intensities. The need for highly accurate molecular rovibrational line lists is two-fold: a) assignment of the many rovibrational lines for common stable molecules especially those that exhibit a large amplitude motion, such as NH3, or have a high density of states such as SO2; and b) characterization of the atmospheres of exoplanets which will be one of the main areas of research in astronomy in the coming decades. The first motivation arises due to the need to eliminate lines due to common molecules in an astronomical observation in order to identify lines from new molecules, while the second motivation arises due to the need to obtain accurate molecular opacities in order to characterize the atmosphere of an exoplanet. The BTRHE strategy first consists of using high quality ab initio quantum chemical methods to obtain a global potential energy surface (PES) and dipole moment surface (DMS) that contains the proper physics. The global PES is then refined using a subset of the reliable high-resolution experimental data. The refined PES then gives energy level predictions to an accuracy similar to the reproduction accuracy of the experimental data used in the refinement step in the interpolation region (i.e., within the range of the experimental data used in the refinement step). The accuracy of the energy levels will slowly degrade as they are extrapolated to spectral regions beyond the high-resolution experimental data used in the refinement step. However, because the degradation is slow, the predicted energy levels can be used to assign new high-resolution experiments, and the data from these can then be used in a subsequent refinement step. In this way, the global PES eventually can yield highly accurate energy levels for all desired spectral regions including to very high energies and high J values. We show that IR intensities computed with the BTRHE rovibrational wavefunctions and the DMS can be very accurate provided one has minimized the fitting error of the DMS and tested the completeness of the DMS. Some examples of our work on NH3, CO2, and SO2 are given to highlight the usefulness of the BTRHE strategy and to provide ideas on how to further improve its predictive power in the future. In particular, it is shown how successive refinement steps, once new high-resolution data is available, can lead to PESs that yield highly accurate transition energies to larger spectral regions. The importance of including non-adiabatic corrections to reduce the J-dependence of errors for H-containing molecules is shown with work on NH3. Another very important aspect of the BTRHE approach is the consistency across isotopologues, which allows for highly accurate line lists for any isotopologue once one is obtained for the main isotopologue (which has more high-resolution data available for refinement).

Xinchuan Huang↗
DOE OSTI2016

Characterization of tenofovir, tenofovir disoproxil fumarate and emtricitabine in aqueous solutions containing sodium ions using ESI-MS, NMR and Ab initio calculations

Here, we have discerned some characteristics of protonated and sodiated forms of emtricitabine tenofovir and tenofovir disoproxil fumarate in aqueous solutions using mass spectrometry/mass spectrometry (MS/MS), nuclear magnetic resonance spectroscopy (NMR) and Ab initio calculations. Protonated species exhibited different mass spectral characteristics from sodiated species for the three antiretroviral drugs. Sodiation of the three compounds yields extremely stable conformers which require high excitation energies to detach the sodium cation using MS/MS. In the case of emtricitabine (FTC), collision induced dissociation (CID) of the protonated species with m/z 248 yielded m/z 130 as the major product ion by losing 2-ethyl-1,3-oxathiolane. MS/MS of the sodiated molecular ion [M + Na] + with m/z 270 would be expected to yield [M + H] + i.e. m/z 248 by losing the Na + cation but instead lost 2-ethyl-1,3-oxathiolane and left the Na + cation well sequestered within the ionic complex. Similar observations were made with tenofovir disoproxil fumarate (TDF) and tenofovir (TFV). Ab initio calculations showed in the case of sodiated TDF and TFV that, the sodium could only be cleaved when a nucleophilic species i.e. PO 2 to which it was coordinated to, fragmented requiring activation energy of 1.5 eV. The potential energy barrier to remove the Na + cation alone was too high to allow the cleavage to proceed. NMR data showed that when TFV or TDF coordinates with Na + ions at one of the nitrogens at position 1 in the purine moiety of the molecule, it also interacts with oxygens of the phospho group to form an ionic complex and perturbs the chemical environments of several atoms in the periphery. The abundance of sodium cations and their strong interactions with the core structures of FTC, TDF and TFV make sodiated species as the best candidates for their quantitation using MS/MS especially in biological fluids. Evidence for improved sensitivity when sodiated species of some of the antiretroviral (ARV) drugs were used for quantitation exists in the literature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

First principles prediction of amorphous phases using evolutionary algorithms

We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within ∼2% of those reported by ab initio MD calculations and experimental studies.

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

Microphysics studies for direct-drive inertial confinement fusion

Accurate and self-consistent knowledge of material properties under high-energy-density (HED) conditions is crucial to reliably understand and design inertial confinement fusion (ICF) targets through radiation–hydrodynamic simulations. For direct-drive ICF target designs, the fuel deuterium–tritium mixtures and ablator materials can undergo a wide range of density and temperature conditions. Their properties under extreme HED conditions, including the equation of state, thermal conductivity, opacity, and stopping power, are the necessary inputs for ICF simulations. To improve the predictive capability of radiation–hydrodynamic codes for direct-drive ICF simulations, we have performed systematic ab initio studies on the static, transport, and optical properties of deuterium (D2) and ablator materials such as polystyrene (CH), beryllium (Be), and silicon (Si), using first-principles methods. The obtained material properties, being favorably compared with existing experimental data, have been implemented into radiation–hydrodynamic codes. This article gives a brief review on how these microphysics studies affect the 1-D radiation–hydrodynamic predictions of direct-drive ICF implosions on the OMEGA Laser System.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Density functional theory based neural network force fields from energy decompositions

In order to develop force fields (FF) for molecular dynamics simulations that retain the accuracy of ab initio density functional theory (DFT), in this work we developed a machine learning protocol based on an energy decomposition scheme that extracts atomic energies from DFT calculations. Our DFT to FF (DFT2FF) approach provides almost hundreds of times more data for the DFT energies, which dramatically improves accuracy with less DFT calculations. In addition, we use piecewise cosine basis functions to systematically construct symmetry invariant features into the neural network model. We illustrate this DFT2FF approach for amorphous silicon where only 800 DFT configurations are sufficient to achieve an accuracy of 1 meV/atom for energy and 0.1 eV/A for forces. We then use the resulting FF model to calculate the thermal conductivity of amorphous Si based on long molecular dynamics simulations. The dramatic speedup in training in our DFT2FF protocol allows the adoption of a simulation paradigm where an accurate and problem specific FF for a given physics phenomenon is trained on-the-spot through a quick DFT precalculation and FF training.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1975

Phenomenological treatments of cross-sections for proton and hydrogen impact below 1 keV on molecular nitrogen

An analytic independent-particle model is used to construct static potentials to describe the interaction of hydrogen-like ions with atoms and molecules. Parameters of the ion-atom potential are determined from ab initio total energy minimization procedure. The elastic scattering of He(+) from Ne and Ar is investigated as a test case and comparison is made with experiment. The model is then used in conjunction with low energy H(+)-N2 experimental data to construct differential and total cross-sections for the scattering of protons and hydrogen in the energy range of 10 eV to 1 keV from molecular nitrogen. Analytic forms are used to parametrize these cross-sections to facilitate their use in the calculation of energy deposition by protons and hydrogen atoms in atmospheric gases.

Porter, H. S.↗
NASA NTRS2000

Recent Theoretical Studies On Excitation and Recombination

New advances in the theoretical treatment of atomic processes in plasmas are described. These enable not only an integrated, unified, and self-consistent treatment of important radiative and collisional processes, but also large-scale computation of atomic data with high accuracy. An extension of the R-matrix work, from excitation and photoionization to electron-ion recombination, includes a unified method that subsumes both the radiative and the di-electronic recombination processes in an ab initio manner. The extensive collisional calculations for iron and iron-peak elements under the Iron Project are also discussed.

Pradhan, Anil K.↗
DOE OSTI2014

An analytic mapping of oligomer potential energy surfaces to an effective Frenkel model

While the use of Frenkel-type models for semiconducting polymer assemblies and related molecular aggregates is well established, the direct parametrization of such models based on electronic structure data is attempted less frequently. In this work, we develop a systematic mapping procedure which is adapted to J-type and H-type homo-aggregate systems. The procedure is based upon the analytic solution of an inverse eigenvalue problem for an effective Frenkel Hamiltonian with nearest-neighbor couplings. Vibronic interactions are included for both site-local and site-correlated modes. For illustration, an application is presented to the excited-state ab initio potential energy surfaces (PESs) of an oligothiophene octamer. The procedure performs a pointwise mapping of the PESs of oligomers of arbitrary chain length n, provided that the electronic ground state and any two of the n lowest adiabatic states of the excitonic manifold of interest are known. These three states are reproduced exactly by the procedure while the remaining n − 2 states of the excitonic manifold can be predicted. Explicit conditions are derived permitting to verify whether a given data set is compatible with the effective Frenkel model under study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

The ever-increasing power of modern super-computers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.

Chan, Henry↗
DOE OSTI2021

Importance of nuclear quantum effects on the hydration of chloride ion

Path-integral ab initio molecular dynamics (PI-AIMD) calculations have been employed to probe the nature of chloride ion solvation in aqueous solution. Nuclear quantum effects (NQEs) are shown to weaken hydrogen bonding between the chloride anion and the solvation shell of water molecules. As a consequence, the disruptive effect of the anion on the solvent water structure is significantly reduced compared to what is found in the absence of NQEs. Additionally, the chloride hydration structure obtained from PI-AIMD agrees well with information extracted from neutron scattering data. In particular, the observed satellite peak in the hydrogen-chloride-hydrogen triple angular distribution serves as a clear signature of NQEs. The present results suggest that NQEs are likely to play a crucial role in determining the structure of saline solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations

We present GPR_calculator, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of GPR_calculator, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at https://github.com/MaterSim/GPR_calculator.

Gaussian process regression↗
DOE OSTI2024

Quenching of the quantum p -strength in light exotic nuclei

One-nucleon spectroscopic overlaps, their strength or spectroscopic factors (SFs), and nucleon removal cross sections for light nuclei in the mass range A≤12 were evaluated using the fully correlated Quantum Monte Carlo (QMC) wave functions (WFs). Harder (AV18+UX) and softer (NV2+3) bare interactions were used providing consistent results. The SFs were also taken from simple Shell Model (SM) calculations. This structure information was incorporated in the standard three-body Faddeev/Alt-Grassberger-Sandhas reaction formalism to evaluate the (p,pN) cross sections. The results further our understanding of the quenching of the quantum p-shell strength obtained from structure and reactions. We have found the ratios of the total QMC sums of SFs to the SM ones to be uniform and ∼ 3/4. The corresponding ratios of the sums Below Particle Threshold (BPT) of SFs, as well as of total cross sections, deviate strikingly from the uniform trend in some special cases. We find these ratios to be close to unit for 9 Li → 8 Li + n and 9 C → 8 B + p. In contrast, they are strongly reduced for 11 C → 10 C + n and 11 B → 10 Be + p mirror transitions, resulting from the quenching of the strength for low-lying 2 + final state transitions. The QMC theoretical cross section BPT for 11 C(p,pn) is about two times smaller than the experimental data.

(p,pN) reactions↗
DOE OSTI2022

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C 2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Finally, among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.

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

The equation of state and shock-driven decomposition of polymethylmethacrylate (PMMA)

We present new equations of state (EOS) for polymethylmethacrylate and its shock-driven decomposition products, generated in both tabular form and as linear U S – u p fits to all previously available shock data. Different approaches are taken for materials shocked above and below the apparent threshold for chemical reactions at pressures P ≈ 30 GPa. Both EOSs are calibrated to and compared with a broad array of experimental results, as well as new ab initio molecular dynamics simulations presented here. Comparisons are good in most cases, and we focus our particular attention on those observables that test the distinction between reactants and products.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High temperature stability and transport characteristics of hydrogen in alumina via multiscale computation

Here, the impact of hydrogen charge states on the stability and transport characteristics of hydrogen interstitials in alumina polymorphs is evaluated by multiscale computational methods including density functional theory (DFT), ab initio molecular dynamics (AIMD) and machine learned force fields. Thermodynamic calculations show that the protonic H i +1 interstitial is the most stable defect species for most values of the electronic bandgap in both and amorphous alumina (Al 2 O 3 ). Further, active learned Gaussian approximation potentials (GAP) were developed using AIMD data to study temperature dependent long time proton diffusion in alumina. Diffusivity calculations from GAP-MD simulations are found to be comparable with of the AIMD data, while being ~340 times faster and scalable to larger systems. Comparisons with diffusivity values for other interstitial charge states (H i 0 and H i -1 ) and published experimental literature indicate that H i +1 diffusion is the likely mechanism of hydrogen transport. A good agreement is obtained between H i +1 diffusivity calculated in α-Al 2 O 3 from DFT: 5.05 10 -3 exp(-0.81 eV/k B /T) cm 2 /s and reported experiment: 9.7X10 -4 exp(-0.83 eV/k B /T) cm 2 /s. Computationally and experimentally calculated energy barriers (0.81 and 0.83 eV respectively) only differ by 2.5%. Similarly, the pre-exponential diffusion coefficients only differ by 0.5 orders of magnitude. Moreover, the diffusivity of H i +1 in amorphous Al 2 O 3 in the 1000–2000 K range is calculated to be 2.53X10 -2 exp(-0.89 eV/k B /T), just one order of magnitude higher than the corresponding value in α-Al 2 O 3 . This suggests that local structural disorder does not significantly affect the energy landscape and diffusion behavior of H i +1 in Al 2 O 3 . Overall, these results show promise for the application of alumina polymorphs as hydrogen permeation barriers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗