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

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

Authors :
Devergne T
Magrino T
Pietrucci F
Saitta AM
Source :
Journal of chemical theory and computation [J Chem Theory Comput] 2022 Sep 13; Vol. 18 (9), pp. 5410-5421. Date of Electronic Publication: 2022 Aug 05.
Publication Year :
2022

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 one 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 classic Strecker-cyanohydrin mechanism for glycine synthesis in water solution, where the main precursors are formaldehyde and hydrogen cyanide. We demonstrate that indistinguishable ab initio quality results are obtained, thanks to the ML subprotocol, at about 1 order of magnitude less of computational load.

Details

Language :
English
ISSN :
1549-9626
Volume :
18
Issue :
9
Database :
MEDLINE
Journal :
Journal of chemical theory and computation
Publication Type :
Academic Journal
Accession number :
35930696
Full Text :
https://doi.org/10.1021/acs.jctc.2c00400