Supporting Data for "Does a Machine-Learnt Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"

(2022) Supporting Data for "Does a Machine-Learnt Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins". Zenodo doi:10.5281/zenodo.7015273 [Dataset]

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Published date: 28 November 2022

Related URLs:

https://doi.org/10.1021/acs.jcim.2c01510

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Identifiers

Local EPrints ID: 473146

URI: http://eprints.soton.ac.uk/id/eprint/473146

DOI: doi:10.5281/zenodo.7015273

PURE UUID: 1f05d21c-e02c-41c3-9efc-bac7df57a2f7

ORCID for Chris-Kriton Skylaris:

orcid.org/0000-0003-0258-3433

ORCID for Jonathan Essex:

orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 10 Jan 2023 18:47

Last modified: 06 May 2023 01:42

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Contributors

Contributor: Chris-Kriton Skylaris

Contributor: Joao Pedro Dos Santos Morado

Contributor: Paul N. Mortenson

Contributor: Jonathan Essex

Contributor: J. Willem M. Nissink

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