Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics-Reference-Cited by-同舟云学术

Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics

Published:2022 Issue:28 Volume:13 Page:8265-8273
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Shi Yu¹^ORCID,Lam Stephen T.²^ORCID,Beck Thomas L.³^ORCID

Affiliation:

1. Department of Chemistry, University of Cincinnati, Cincinnati, OH 45221, USA

2. Department of Chemical Engineering, University of Massachusetts Lowell, MA 01854, USA

3. National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA

Abstract

Solvation thermodynamics in molten salt is accurately and efficiently predicted by combining ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT).

Funder

National Science Foundation

University of Cincinnati

U.S. Department of Energy

Nuclear Energy University Program

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC02227C

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