Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution - Sorbonne Université Access content directly
Journal Articles Journal of Chemical Theory and Computation Year : 2022

Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution

Abstract

The study of the thermodynamics, kinetics, and microscopic mechanisms of chemical reactions in solution requires the use of advanced free-energy methods for predictions to be quantitative. This task is however a formidable one for atomistic simulation methods, as the cost of quantum-based ab initio approaches, to obtain statistically meaningful samplings of the relevant chemical spaces and networks, becomes exceedingly heavy. In this work, we critically assess the optimal structure and minimal size of an ab initio training set able to lead to accurate free energy profiles sampled with neural network potentials. The results allow to propose an ab initio protocol where the ad hoc inclusion of a machine-learning (ML)-based task can significantly increase the computational efficiency, while keeping the ab initio accuracy and, at the same time, avoiding some of the notorious extrapolation risks in typical atomistic ML approaches. We focus on two representative, and computationally challenging, reaction steps of the
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Dates and versions

hal-03927894 , version 1 (06-01-2023)

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Timothée Devergne, Théo Magrino, Fabio Pietrucci, A. Marco Saitta. Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution. Journal of Chemical Theory and Computation, 2022, 18 (9), pp.5410-5421. ⟨10.1021/acs.jctc.2c00400⟩. ⟨hal-03927894⟩
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