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Title: On-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH

Journal Article · · Physics of Plasmas
DOI: https://doi.org/10.1063/5.0204229 · OSTI ID:2567815

We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the training data generated from the Kohn–Sham density functional theory (DFT) using the Gauss spectral quadrature method, within which we calculate energies, atomic forces, and stresses. We verify the accuracy of the formalism by comparing the predicted properties of warm dense carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that ab initio MD simulations of WDM can be accelerated by up to three orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the diffusion coefficients and shear viscosity of CH at a density of 1 g/cm3 and temperatures in the range of 75 000–750 000 K. We find that the self- and inter-diffusion coefficients and the viscosity obey a power law with temperature, and that the diffusion coefficient results suggest a weak coupling between C and H in CH. In addition, we find agreement within standard deviation with previous results for C and CH but disagreement for H, demonstrating the need for ab initio calculations as presented here.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344; NA0004128
OSTI ID:
2567815
Report Number(s):
LLNL--JRNL-865772; 043905
Journal Information:
Physics of Plasmas, Journal Name: Physics of Plasmas Journal Issue: 4 Vol. 31; ISSN 1070-664X
Publisher:
American Institute of PhysicsCopyright Statement
Country of Publication:
United States
Language:
English

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