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Exploration of elastic moduli of molecular crystals via database screening by pretrained neural network potential

25 October 2023, Version 1
Working Paper
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

This paper presents the database screening of elastic moduli of molecular crystals using a pretrained neural network potential. The calculated elastic moduli were sufficiently consistent with experiment values, better than Hartree-Fock calculations. The database screening suggested crystals with large and small moduli.

Keywords

Neural Network Potential
Elastic Modulus
Molecular Crystals

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Version History

Oct 25, 2023 Version 1

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The content is available under CC BY NC 4.0 [opens in a new tab]

DOI

10.26434/chemrxiv-2023-lbdrm
D O I: 10.26434/chemrxiv-2023-lbdrm [opens in a new tab]

Funding

Japan Society for the Promotion of Science
19K23638, 20H04677, 22K14747
Waseda University
2019C-646, 2020C-530, 2021C-404, 2022C-313
ENEOS Corporation

Author’s competing interest statement

The author received a joint research funding from ENEOS corporation and licensed Matlantis for using PFP model from Preferred Computational Chemistry, Inc., a joint venture of Preferred Networks, Inc. and ENEOS corporation.

Ethics

The author(s) have declared ethics committee/IRB approval is not relevant to this content

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