SEARCH · Engineering Papers

Results for “ab initio data”

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.

SourceResearch connectionFrom yearThrough year

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

At least 793 records · Page 44

All sources NASA NTRS DOE OSTI

DOE OSTI2014

Quantum Monte Carlo Calculations of Electromagnetic Transitions in Light Nuclei

Recently, we presented ab initio quantum Monte Carlo calculations of magnetic moments and M1 transitions in A?10 nuclei, which include two-body meson-exchange currents' contributions of one- and two-pion range, derived from chiral effective field. These corrections are found to always improve the theoretical predictions, leading to a very good agreement with the experimental data. Here, we report on a study, carried out within the same framework, of M1 transitions in low-lying states of 8Be. We also present a number of E2 transition calculations, evaluated in impulse approximation, with emphasis on transitions involving the 8Be resonant excited states at ?3 MeV and ?11 MeV.

Pastore, Saori↗

DOE OSTI2014

Quantum Monte Carlo calculations of electromagnetic transitions in light nuclei

Recently, we presented ab initio quantum Monte Carlo calculations of magnetic moments and M1 transitions in A <= 10 nuclei, which include two-body meson-exchange currents' contributions of one- and two-pion range, derived from chiral effective field. These corrections are found to always improve the theoretical predictions, leading to a very good agreement with the experimental data. Here, we report on a study, carried out within the same framework, of M1 transitions in low-lying states of 8Be. We also present a number of E2 transition calculations, evaluated in impulse approximation, with emphasis on transitions involving the 8Be resonant excited states at ~ 3 MeV and ~ 11 MeV.

Pastore, Saori↗

DOE OSTI2019

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

Abstract It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4 × 4 and 5 × 5 supercells they accurately predict bandgaps, with a R 2 of >90% and root-mean-square error of ~0.1 eV. The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6 × 6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.

Dong, Yuan↗

DOE OSTI2026

Halo Nuclei from Ab Initio Nuclear Theory

A realistic description of halo nuclei, characterized by low-lying breakup thresholds, requires a proper treatment of continuum effects. We have developed an ab initio approach, the No-Core Shell Model with Continuum (NCSMC), capable of describing both bound and unbound states in light nuclei in a unified way. With chiral two- and three-nucleon interactions as the only input, we can predict the structure and dynamics of halo and other light nuclei and, by comparing to available experimental data, test the quality of chiral nuclear forces. We review NCSMC calculations of weakly bound states and resonances of the exotic halo nuclei 6He, 8B, 11Be, and 15C. For the latter, we discuss its production in the capture reaction 14C(n,𝛾 )15C. We highlight the challenges of a description of 6He as a Borromean n-n-4He system. Finally, we present our calculations of excited states in 10Be exhibiting a one-neutron halo structure and a large scale No-Core Shell Model investigation of 11Li as a precursor of a full n-n-9Li NCSMC study.

Navrátil, Petr↗

NASA NTRS2001

The A6Sigma+ - X6Sigma+ Transition of CrH, Einstein Coefficients and an Improved Description of the A State

The spectrum of CrH has been reinvestigated in the 9000-15000/cm region using the Fourier transform spectrometer of the National Solar Observatory. The 1-0 and 1-1 bands of the A6Sigma+ - X6Sigma+ transition have been measured and improved spectroscopic constants have been determined. A value for the 2-0 band origin has been obtained from the band head using estimated spectroscopic constants. These data provide a set of much improved equilibrium vibrational and rotational constants for the A6Sigma+ state. An accurate description of the A-X transition has been obtained using a multi-reference configuration interaction approach. The inclusion of both scalar relativity and Cr 3s3p correlation are required to obtain a good description of both states. The ab initio computed Einstein coefficients and radiative lifetimes are reported.

Bauschlicher, Charles W., Jr.↗

DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗

DOE OSTI2022

Real-space visualization of short-range antiferromagnetic correlations in a magnetically enhanced thermoelectric

Short-range magnetic correlations can significantly increase the thermopower of magnetic semiconductors, representing a noteworthy development in the decades-long effort to develop high-performance thermoelectric materials. Here, we reveal the nature of the thermopower-enhancing magnetic correlations in the antiferromagnetic semiconductor MnTe. Using magnetic pair distribution function analysis of neutron scattering data, we obtain a detailed, real-space view of robust, nanometer-scale, antiferromagnetic correlations that persist into the paramagnetic phase above the Neel temperature $T_N$ = 307 K. In this work, the magnetic correlation length in the paramagnetic state is significantly longer along the crystallographic c axis than within the ab plane, pointing to anisotropic magnetic interactions. Ab initio calculations of the spin-spin correlations using density functional theory in the disordered local moment approach reproduce this result with quantitative accuracy. These findings constitute the first real-space picture of short-range spin correlations in a magnetically enhanced thermoelectric and inform future efforts to optimize thermoelectric performance by magnetic means.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Abstract Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding. Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD). Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach. Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies. Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium.

Zong, Hongxiang↗

DOE OSTI2017

Electric Dipole Polarizability of Ca 48 and Implications for the Neutron Skin

The electric dipole strength distribution in 48 Ca between 5 and 25 MeV has been determined at RCNP, Osaka, from proton inelastic scattering experiments at forward angles. Combined with photoabsorption data at higher excitation energy, this enables the rst extraction of the electric dipole polarizability D( 48 Ca) = 2:07(22) fm 3 . Remarkably, the dipole response of 48 Ca is found to be very similar to that of 40 Ca, consistent with a small neutron skin in 48 Ca. The experimental results are in good agreement with ab initio calculations based on chiral e ective eld theory interactions and with state-of-the-art density-functional calculations, implying a neutron skin in 48 Ca of 0:14 - 0:20 fm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

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 OSTI2017

Accurate Theoretical Methane Line Lists in the Infrared up to 3000 K and Quasi-continuum Absorption/Emission Modeling for Astrophysical Applications

Modeling atmospheres of hot exoplanets and brown dwarfs requires high- T databases that include methane as the major hydrocarbon. We report a complete theoretical line list of {sup 12}CH{sub 4} in the infrared range 0–13,400 cm{sup −1} up to T {sub max} = 3000 K computed via a full quantum-mechanical method from ab initio potential energy and dipole moment surfaces. Over 150 billion transitions were generated with the lower rovibrational energy cutoff 33,000 cm{sup −1} and intensity cutoff down to 10{sup −33} cm/molecule to ensure convergent opacity predictions. Empirical corrections for 3.7 million of the strongest transitions permitted line position accuracies of 0.001–0.01 cm{sup −1}. Full data are partitioned into two sets. “Light lists” contain strong and medium transitions necessary for an accurate description of sharp features in absorption/emission spectra. For a fast and efficient modeling of quasi-continuum cross sections, billions of tiny lines are compressed in “super-line” libraries according to Rey et al. These combined data will be freely accessible via the TheoReTS information system (http://theorets.univ-reims.fr, http://theorets.tsu.ru), which provides a user-friendly interface for simulations of absorption coefficients, cross-sectional transmittance, and radiance. Comparisons with cold, room, and high- T experimental data show that the data reported here represent the first global theoretical methane lists suitable for high-resolution astrophysical applications.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1987

Theoretical study of the dissociation energy and the red and violet band systems of CN

The dissociation energy (D sub O) of CN is determined to be 7.65 + or - 0.06 eV. This corresponds to delta H sub f (CN) = 105.3 + or - 1.5 kcal/mole, in excellent agreement with Engleman and Rouse (1975), but considerably larger than the recent value deduced from shock-tube studies by Colket (1984). The result is obtained not only from extensive ab initio MRCI calculations using a very large Gaussian basis set, but also from extrapolation of the directly computed value by comparison of computed and experimental results fo NO, C2, and N2. As an additional calibration of the methods, the D sub O value for CN was computed from the corresponding value for CN(-) using the experimental electron affinity data. The lifetime of the nu prime = 0 level of the violet (B 2 sigma + yields X 2 sigma +) system was computed to be 62.4 ns, in good agreement with both experiment and previous calculations. Lifetimes for the red (A 2 pi yields X 2 sigma +) system decrease with increasing nu prime, which is consistent both with the recent experiment and calculations. While the computed lifetimes are significantly longer that those obtained from the experiment, they are shorter than those deduced from an analysis of the solar spectrum. However the D sub O and f (sub OO) are consistent with Lambert's model for the solar spectrum.

Bauschlicher, Charles W., Jr.↗

DOE OSTI2024

Breakthrough Conductivity Enhancement in Deep Eutectic Solvents via Grotthuss–Type Proton Transport

There is an increasing demand for the development of ion-conducting electrolytes for energy storage systems. Much attention is directed toward deep eutectic solvents as potential candidates. In the search for highly conductive systems, the possibility of designing deep eutectic solvents with Grotthuss-type proton transport is widely overlooked. Herein, ethaline, a mixture of choline chloride and ethylene glycol is used in a 1:2 molar ratio, to induce a significant conductivity increase with the addition of water and sulfuric acid (H 2 SO 4 ). The achieved breakthrough conductivity is analyzed experimentally and simulated with ab initio molecular dynamics (AIMD). At sufficient water content, an H-bonding network is formed that leads to a significant breakthrough conductivity based on H 2 SO 4 -derived proton transfer following the long-established Grotthuss proton transport mechanism. This result is substantiated by the positive deviation from the ideal KCl line in the Walden plot. Specifically, the data series positioned above the reference line indicates a Grotthuss mechanism in action. The AIMD simulations demonstrate proton transfer between water and ethylene glycol, supported by simulation frames captured at various times.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Oxidation of methylamine

A detailed chemical kinetic model for oxidation of methylamine has been developed, based on theoretical work and a critical evaluation of data from the literature. In this work, the rate coefficients for the reactions of CH 3 NH 2 + O 2 → CH 2 NH 2 / CH 3 NH + HO 2 , CH 3 NH 2 + H → CH 3 + NH 3 , CH 3 NH → CH 2 NH 2 , and CH 3 NH + O 2 → CH 2 NH + HO 2 were calculated from ab initio theory. The mechanism was validated against experimental results from batch reactors, flow reactors, shock tubes, and premixed flames. The model predicts satisfactorily explosion limits for CH 3 NH 2 and its oxidation in a flow reactor. However, oxidation in the presence of nitric oxide, which strongly promotes reaction at lower temperatures, is only described qualitatively. Furthermore, calculated flame speeds are higher than reported experimental values; the model does not capture the inhibiting effect of the NH 2 group in CH 3 NH 2 compared to CH 4 . More work is desirable to confirm the products of the CH 3 NH + NO reaction and to look into possible pathways to NH 3 in methylamine oxidation.

10 SYNTHETIC FUELS↗

DOE OSTI2019

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common “black box” type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Hole Transfer Dynamics from a CdSe/CdS Quantum Rod to a Tethered Ferrocene Derivative

Hole transfer between a CdSe/CdS core/shell semiconductor nanorod and a surface-ligated alkyl ferrocene is investigated by a combination of ab initio quantum chemistry calculations and electrochemical and time-resolved photoluminescence measurements. The calculated driving force for hole transfer corresponds well with electrochemical measurements of nanorods partially ligated by 6-ferrocenylhexanethiolate. The calculations and the experiments suggest that single step hole transfer from the valence band to ferrocene is in the Marcus inverted region. Additionally, time-resolved photoluminescence data suggest that two-step hole transfer to ferrocene mediated by a deep trap state is unlikely. However, the calculations also suggest that shallow surface states of the CdS shell could play a significant role in mediating hole transfer as long as their energies are close enough to the nanorod highest occupied molecular orbital energy. Regardless of the detailed mechanism of hole transfer, our results suggest that holes may be extracted more efficiently from well-passivated nanocrystals by reducing the energetic driving force for hole transfer, thus minimizing energetic losses.

Chemistry↗

NASA NTRS1992

A global potential energy surface for ArH2

We describe a simple analytic representation of the ArH2 potential energy surface which well reproduces the results of extensive ab initio electronic structure calculations. The analytic representation smoothly interpolates between the dissociated H2 and strong bonding limits. In the fitting process, emphasis is made on accurately reproducing regions of the potential expected to be important for high temperature (ca. 3000 K) collision processes. Overall, the anisotropy and H2 bond length dependence of the analytic representation well reproduce the input data.

Schwenke, David W.↗

NASA NTRS1993

A global potential energy surface for ArH2

Schwenke, David W.↗