Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials

19 July 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials (MLPs) to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We demonstrate the versatility of this strategy by applying it to investigate a Diels-Alder reaction in water and methanol. The generated MLPs exhibit excellent agreement with experimental data and provide insights into the differences in reaction rates observed between the two solvents. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner.


machine learning potentials
active learning
chemical reactions
explicit solvents

Supplementary materials

Supporting information
Supporting information describing hyperparameters used, performance of tested selectors and computational details on the tested systems.


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