eprintid: 10192066
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/20/66
datestamp: 2024-05-09 15:59:33
lastmod: 2024-05-09 15:59:33
status_changed: 2024-05-09 15:59:33
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Edeling, Wouter
creators_name: Vassaux, Maxime
creators_name: Yang, Yiming
creators_name: Wan, Shunzhou
creators_name: Guillas, Serge
creators_name: Coveney, Peter V
title: Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F56
note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
abstract: Uncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated. Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability, it is essential that practitioners pay attention to the issues concerned. Here a UQ study is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters, which affect key quantities of interest, including material properties and binding free energy predictions in drug discovery and personalized medicine. Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes, the sensitivity of the input parameters is ranked. Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed. This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters.
date: 2024-05-03
date_type: published
publisher: Springer Science and Business Media LLC
official_url: https://doi.org/10.1038/s41524-024-01272-z
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2273517
doi: 10.1038/s41524-024-01272-z
lyricists_name: Wan, Shunzhou
lyricists_name: Coveney, Peter
lyricists_id: SWANX17
lyricists_id: PCOVE58
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: npj Computational Materials
volume: 10
article_number: 87
issn: 2057-3960
citation:        Edeling, Wouter;    Vassaux, Maxime;    Yang, Yiming;    Wan, Shunzhou;    Guillas, Serge;    Coveney, Peter V;      (2024)    Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields.                   npj Computational Materials , 10     , Article 87.  10.1038/s41524-024-01272-z <https://doi.org/10.1038/s41524-024-01272-z>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10192066/1/s41524-024-01272-z.pdf