An artificial neural network using multi-head intermolecular attention for predicting chemical reactivity of organic materials

Abstract

Selecting functional materials that are chemically compatible with each other is a prerequisite for the assembly of multi-component systems and is crucial for their long-term system stability. In the design of new organic-based batteries, one of the promising post-lithium-ion battery systems, the exploration of organic compounds for the electrode and electrolyte should consider not only their intrinsic electrochemical activity/stability but also the compatibility among the constituting components. Herein, we report an extensive scheme of predicting the chemical reactivities of any combination of two organic compounds by employing the so-called Intermolecular Reaction Rate Network (ImRRNet). This new artificial neural network (ANN) platform exploits the novel intermolecular multi-head attention method to predict the precise reaction rate constant between two organic chemicals and was trained with a large chemical space of 175 987 datasets on nucleophilicity and electrophilicity. The intermolecular multi-head attention method successfully identified the local substructure that primarily determines the chemical reactivity of organic molecules by providing a greater attention score in the specific position. The prediction accuracy of ImRRNet was observed to be remarkably higher (mean absolute error of 0.5760) than that of other previous ANN models (>0.94), validating its efficacy for practical employment in the design of multi-component organic-based rechargeable batteries.