Machine learning prediction of a chemical reaction over 8 decades of energy

TL;DR

This paper develops neural network models to predict the energy-dependent outcomes of atom recombination reactions, achieving high accuracy across a broad energy range and demonstrating the potential of machine learning to understand complex chemical reaction dynamics.

Contribution

Introduces neural networks trained to predict opacity functions of termolecular atom recombination reactions, filling a gap in machine learning models for such reactions.

Findings

Models predict reaction outcomes with less than 10% error.

Accurate predictions extend beyond training energy ranges.

Machine learning captures underlying physics of reaction dynamics.

Abstract

Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions. Accordingly, we introduce neural networks trained to predict opacity functions of atom recombination reactions. Our models predict the recombination of Sr$^+$ + Cs + Cs $\rightarrow$ SrCs$^+$ + Cs and Sr$^+$ + Cs + Cs $\rightarrow$ Cs$_2$ + Sr$^+$ over multiple orders of magnitude of energy, yielding overall results with a relative error $\lesssim 10\%$. Even far beyond the range of energies seen during training, our models predict the atom recombination reaction rate accurately. As a result, the machine is capable of learning the physics behind the atom recombination reaction dynamics.