Towards Transferable Machine Learning Interatomic Potentials for Reactive Organic Chemistry in Solution

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Reactions in liquid phase are central to research and industry involving synthetic organic chemistry. Solvents influence reaction mechanism and rate in complicated ways involving non-covalent interactions and proton transfers. Solvent effects are not yet completely understood. Computer simulation can aid the understanding of solvent effects through free energy calculations and treatment of the full system at a high level of theory. This is commonly done using ab initio molecular dynamics (AIMD), which has a limited scope of application due to high computational cost. Machine learning interatomic potentials (MLIPs) are becoming a common tool for accurate condensed phase simulation, extending the time- and length-scale accessible to simulation without significant loss of accuracy compared with AIMD. The key challenge to developing an MLIP is the efficient sampling of relevant PES. There have been attempts at building a general ML potential as well as attempts at creating MLIPs for a specific condensed phase reaction. The high flexibility of MLIPs opens the door to FFs that accurately represent a large fraction of the chemistry spanned by a selection of chemical elements. There has not been attempt to build a general reactive MLIP for studying organic reactions in solution. We will develop a strategy to efficiently sample configurations that represent the PES of a chosen solution-phase reactive system. We will then generalise this approach to build a general-purpose MLIP that achieves reasonable accuracy across different reactant-solvent systems and does not need to be re-trained for each new application. Finally, we hope to use our models to carry out calculations of reactive systems relevant to modern organic chemistry research.