TY  - JOUR
VL  - 10
N1  - 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/.
JF  - npj Computational Materials
PB  - Springer Science and Business Media LLC
A1  - Edeling, Wouter
A1  - Vassaux, Maxime
A1  - Yang, Yiming
A1  - Wan, Shunzhou
A1  - Guillas, Serge
A1  - Coveney, Peter V
Y1  - 2024/05/03/
SN  - 2057-3960
TI  - Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields
AV  - public
N2  - 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.
ID  - discovery10192066
UR  - https://doi.org/10.1038/s41524-024-01272-z
ER  -