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Results for “ab initio modeling”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Direct kinetic measurements and theoretical predictions of an isoprene-derived Criegee intermediate

Isoprene has the highest emission into Earth’s atmosphere of any nonmethane hydrocarbon. Atmospheric processing of alkenes, including isoprene, via ozonolysis leads to the formation of zwitterionic reactive intermediates, known as Criegee intermediates (CIs). Direct studies have revealed that reactions involving simple CIs can significantly impact the tropospheric oxidizing capacity, enhance particulate formation, and degrade local air quality. Methyl vinyl ketone oxide (MVK-oxide) is a four-carbon, asymmetric, resonance-stabilized CI, produced with 21 to 23% yield from isoprene ozonolysis, yet its reactivity has not been directly studied. We present direct kinetic measurements of MVK-oxide reactions with key atmospheric species using absorption spectroscopy. Direct UV-Vis absorption spectra from two independent flow cell experiments overlap with the molecular beam UV-Vis-depletion spectra reported recently [M. F. Vansco, B. Marchetti, M. I. Lester, J. Chem. Phys. 149, 44309 (2018)] but suggest different conformer distributions under jet-cooled and thermal conditions. Comparison of the experimental lifetime herein with theory indicates only the syn -conformers are observed; anti -conformers are calculated to be removed much more rapidly via unimolecular decay. We observe experimentally and predict theoretically fast reaction of syn -MVK-oxide with SO 2 and formic acid, similar to smaller alkyl-substituted CIs, and by contrast, slow removal in the presence of water. We determine products through complementary multiplexed photoionization mass spectrometry, observing SO 3 and identifying organic hydroperoxide formation from reaction with SO 2 and formic acid, respectively. The tropospheric implications of these reactions are evaluated using a global chemistry and transport model.

09 BIOMASS FUELS↗
DOE OSTI2022

Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A phaseless auxiliary-field quantum Monte Carlo perspective on the uniform electron gas at finite temperatures: Issues, observations, and benchmark study

We investigate the viability of the phaseless finite-temperature auxiliary-field quantum Monte Carlo (ph-FT-AFQMC) method for ab initio systems using the uniform electron gas as a model. Through comparisons with exact results and FT coupled cluster theory, we find that ph-FT-AFQMC is sufficiently accurate at high to intermediate electronic densities. We show, both analytically and numerically, that the phaseless constraint at FT is fundamentally different from its zero-temperature counterpart (i.e., ph-ZT-AFQMC), and generally, one should not expect ph-FT-AFQMC to agree with ph-ZT-AFQMC in the low-temperature limit. With an efficient implementation, we are able to compare exchange-correlation energies to the existing results in the thermodynamic limit and find that the existing parameterizations are highly accurate. In particular, we found that ph-FT-AFQMC exchange-correlation energies are in better agreement with a known parameterization than is restricted path-integral MC in the regime of Θ ≤ 0.5 and rs ≤ 2, which highlights the strength of ph-FT-AFQMC.

Lee, Joonho (ORCID:0000000296671081)↗
DOE OSTI2021

Neural network potential for Zr–Rh system by machine learning

Zr–Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr–Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. Here, the results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr 77 Rh 23 . Our study lays a foundation for exploring the complex structures in amorphous Zr 77 Rh 23 , which is of great significance for the design and practical application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Artificial intelligence for advanced functional materials: exploring current and future directions

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

Here, Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H 2 O) and heavy water (D 2 O) within the isothermalisobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water are more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H 2 O and D 2 O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Peierls transition, ferroelectricity, and spin-singlet formation in monolayer VOI 2

Using ab initio density functional theory and single-orbital Hubbard model calculations via the density matrix renormalization group method, we systematically studied the monolayer VOI 2 with a 3d 1 electronic configuration. Our phonon calculations indicate that the orthorhombic Pmm2 FE-II phase is the most likely ground state, involving a ferroelectric (FE) distortion along the a axis and V-V dimerization along the b axis. Specifically, the “pseudo Jahn-Teller” effect caused by the coupling between empty V (d xz/yz and d 3z 2 -r 2 ) and O 2p states is proposed as the mechanism that stabilizes the FE distortion from the paraelectric phase. Moreover, the half-filled metallic d xy band displays a Peierls instability along the b axis, inducing a V-V dimerization. Additionally, we found very short-range antiferromagnetic coupling along the V-V chain due to the formation of nearly decoupled spin singlets in the ground state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Isotope Shifts of Radium Monofluoride Molecules

Isotope shifts of 223–226,228 Ra 19 F were measured for different vibrational levels in the electronic transition A 2 II 1/2 ←X 2 Σ + . The observed isotope shifts demonstrate the particularly high sensitivity of radium monofluoride to nuclear size effects, offering a stringent test of models describing the electronic density within the radium nucleus. Ab initio quantum chemical calculations are in excellent agreement with experimental observations. These results highlight some of the unique opportunities that short-lived molecules could offer in nuclear structure and in fundamental symmetry studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Density functional theory (DFT) study of UF 6 hydrolysis: reaction pathways, spectroscopy, and chemical kinetics

Depleted uranium hexafluoride (UF 6 ), a stockpiled byproduct of the nuclear fuel cycle, reacts readily with atmospheric humidity, but the gas-phase reaction mechanism and associated chemical kinetics are poorly understood. During the performance period we undertook development of a state-of-the-art ab initio gas-phase chemical kinetics simulation workflow to model the hydrolysis of uranium hexafluroride (UF 6 ). In doing so, we addressed several outstanding issues in the theoretical treatment of uranium-containing systems. At the outset it was unclear how to generate accurate estimates of kinetic and thermodynamic data for U-containing chemical reactions. Generation of such data has been made routine. Prior to our work, the literature associated with UF 6 hydrolysis were disparate and inaccurate. This body of work provides a modern and comprehensive theoretical assessment of the reaction mechanism, molecular clustering towards deposition, and chemical kinetics. New methodological implementations and software integrations resulting from this work are also highlighted. As much as possible, our predictions were validated against experimental data including particle morphologies, vibrational spectroscopy, atomization enthalpies, and kinetic rate constants. Nevertheless, we were unable to reconcile kinetic measurements with high-accuracy simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Development of a High-Rate Lithium-Air Battery Using a Gaseous CO 2 Reactant

Li-air batteries are considered a potential alternative to Li-ion batteries for transportation applications due to their high theoretical specific energy. Most works in this area focus on use of O 2 as the reactant. However, newer concepts for using gaseous reactants (such as CO 2 , which has a theoretical specific energy density of 1,876 Wh/kg) provide opportunities for further exploration. The main objective of this project was the development of a novel strategy that enables operation of Li-CO 2 batteries at high-capacity and high-rate, with a long-cycle-life. The team was able to: (1) Synthesize two novel transition metal chalcogenide (TMC) catalysts that work in synergy with ionic liquid-based electrolytes to enhance the efficiency of reactions during discharge and charge processes; (2) Fabricate high-porosity cathode electrodes with 3D printing to increase electrode surface area and gas permeability; (3) Develop a multiscale modeling framework that integrates Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties of Li-CO 2 batteries; (4) Assemble a stackable Li-CO 2 pouch-cell able to deliver a capacity of >200 mAh. These achievements were realized through an integrated approach based on materials synthesis, testing, characterization, analysis, and computation. This project produced a thorough understanding of key chemical, electronic, and kinetic parameters that govern the operation of Li- CO 2 batteries in realistic conditions. The methodologies employed, and the insight generated, will be valuable beyond advancing the field of Li-CO 2 batteries

25 ENERGY STORAGE↗
DOE OSTI2024

Exploring the impact of multivalent substitution on high-temperature electrical conductivity in doped lanthanum chromite perovskites: An experimental and ab-initio study

Doped LaCrO 3 perovskites hold promise as robust materials for electrical interconnects and sensor applications in harsh environments. In this work, we investigated the high-temperature behavior of La 1–x Sr x CrO 3 , La 1–x Ca x CrO 3 and La 0.8 Sr 0.2 Cr 1-x Mn x O3 (0.1 ≤ x ≤ 0.4) through a combination of computational modeling and physical characterization up to 1500 °C. Crystalline structural properties were determined and compared with ab-initio calculations, which demonstrated excellent agreement with experimental findings. High-temperature electrical conductivity measurements were performed under different atmospheres. Calcium and strontium/manganese co-doped lanthanum chromites exhibited typical semiconductor exponential trends and conductivity showed proportional correlation with substitutions levels up to 30 %. The DFT modelling was completed up to 1500 °C, including low and high temperature chromite phases and oxygen vacancies insertion. Calculations were correlated with experimental electrical properties. This work expands the understanding of doped lanthanum chromites and paves the way for the development of materials suitable for demanding high-temperature applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

A coupled cluster framework for electrons and phonons

Here, we describe a coupled cluster framework for coupled systems of electrons and harmonic phonons. Neutral and charged excitations are accessed via the equation-of-motion version of the theory. Benchmarks on the Hubbard–Holstein model allow us to assess the strengths and weaknesses of different coupled cluster approximations, which generally perform well for weak to moderate coupling. Finally, we report progress toward an implementation for ab initio calculations on solids and present some preliminary results on finite-size models of diamond with a linear electron–phonon coupling. We also report the implementation of electron–phonon coupling matrix elements from crystalline Gaussian type orbitals within the PySCF program package.

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

Exploring model complexity in machine learned potentials for simulated properties

Abstract Machine learning (ML) enables the development of interatomic potentials with the accuracy of first principles methods while retaining the speed and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks map descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor Li $$_{10}$$ 10 Ge(PS $$_{6}$$ 6 ) $$_{2}$$ 2 (LGPS). We further investigate how these errors affect simulated properties and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Graphical abstract

Rohskopf, A. (ORCID:0000000227128296)↗
NASA NTRS1991

How much hydrogen is there in a white dwarf?

Stratified hydrogen/helium envelope models in diffusive equilibrium are calculated for a 0.6-solar-mass white dwarf for effective temperatures between 10,000 and 80,000 K in order to investigate the observational constraints placed on the total hydrogen mass. Convective mixing is included ab initio in the calculations, and synthetic spectra are used for comparing these models with observational materials. It is shown that evolutionary changes in the surface composition of white dwarfs cannot be explained by a model in which a small amount of hydrogen floats to the surface from initially being mixed in the outer parts of a helium envelope. It is pointed out that the shape of the hydrogen lines can be used for constraining theories of convective overshoot.

Macdonald, James↗
DOE OSTI2020

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a "melt-quench" ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ground state wave functions for single-band Hubbard models from the Gutzwiller conjugate gradient minimisation theory

The Gutzwiller conjugate gradient minimisation (GCGM) theory is an ab initio quantum many-body theory for computing the ground-state properties of infinite systems. Previous applications of GCGM provides satisfying accuracy of ground-state energy of Hubbard models. In the current work, we address the problem of whether the obtained wave function is a good approximation for the true ground state by comparing the correlation functions with the benchmark data. Additionally, our results confirms the accuracy of the reproduced ground state of the regular Hubbard model, but with some exception for the frustrated Hubbard model.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Detection of topological materials with machine learning

Databases compiled using ab initio and symmetry-based calculations now contain tens of thousands of topological insulators and topological semimetals. This makes the application of modern machine learning methods to topological materials possible. Using gradient boosted trees, we show how to construct a machine learning model which can predict the topology of a given existent material with an accuracy of 90%. Such predictions are orders of magnitude faster than actual ab initio calculations. In this work, we use machine learning models to probe how different material properties affect topological features. Notably, we observe that topology is mostly determined by the “coarse-grained” chemical composition and crystal symmetry and depends little on the particular positions of atoms in the crystal lattice. We identify the sources of our model's errors and we discuss approaches to overcome them.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗