Issue 32, 2024

Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials

Abstract

Computational simulation methods based on machine learned potentials (MLPs) promise to revolutionise shape prediction of flexible molecules in solution, but their widespread adoption has been limited by the way in which training data is generated. Here, we present an approach which allows the key conformational degrees of freedom to be properly represented in reference molecular datasets. MLPs trained on these datasets using a global descriptor scheme are generalisable in conformational space, providing quantum chemical accuracy for all conformers. These MLPs are capable of propagating long, stable molecular dynamics trajectories, an attribute that has remained a challenge. We deploy the MLPs in obtaining converged conformational free energy surfaces for flexible molecules via well-tempered metadynamics simulations; this approach provides a hitherto inaccessible route to accurately computing the structural, dynamical and thermodynamical properties of a wide variety of flexible molecular systems. It is further demonstrated that MLPs must be trained on reference datasets with complete coverage of conformational space, including in barrier regions, to achieve stable molecular dynamics trajectories.

Graphical abstract: Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials

Supplementary files

Article information

Article type
Edge Article
Submitted
16 Feb 2024
Accepted
07 Jul 2024
First published
08 Jul 2024
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2024,15, 12780-12795

Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials

C. D. Williams, J. Kalayan, N. A. Burton and R. A. Bryce, Chem. Sci., 2024, 15, 12780 DOI: 10.1039/D4SC01109K

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