Davis Computational Spectroscopy Workflow—From Structure to Spectra

Cavalcante, L. S. R.; Daemen, Luke L.; Goldman, Nir; Moulé, Adam J.

doi:10.1021/acs.jcim.1c00688

Title: Davis Computational Spectroscopy Workflow—From Structure to Spectra

Journal Article · 26 August 2021 · Journal of Chemical Information and Modeling

DOI: https://doi.org/10.1021/acs.jcim.1c00688 · OSTI ID:1860888

^[1]; Daemen, Luke L. ^[2];

^[3];

^[1]

Univ. of California, Davis, CA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of California, Davis, CA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Here, we describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ~100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231; AC52-07NA27344; SC0010419

OSTI ID:: 1860888

Report Number(s):: LLNL-JRNL-821238; 1033060

Journal Information:: Journal of Chemical Information and Modeling, Journal Name: Journal of Chemical Information and Modeling Journal Issue: 9 Vol. 61; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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