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A method for obtaining appropriate reaction coordinates is required toidentify transition states distinguishing product and reactant in complexmolecular systems. Recently, abundant research has been devoted to obtainingreaction coordinates using artificial neural networks from deep learningliterature, where many collective variables are typically utilized in the inputlayer. However, it is difficult to explain the details of which collectivevariables contribute to the predicted reaction coordinates owing to thecomplexity of the nonlinear functions in deep neural networks. To overcome thislimitation, we used Explainable Artificial Intelligence (XAI) methods of theLocal Interpretable Model-agnostic Explanation (LIME) and the game theory-basedframework known as Shapley Additive exPlanations (SHAP). We demonstrated thatXAI enables us to obtain the degree of contribution of each collective variableto reaction coordinates that is determined by nonlinear regressions with deeplearning for the committor of the alanine dipeptide isomerization in vacuum. Inparticular, both LIME and SHAP provide important features to the predictedreaction coordinates, which are characterized by appropriate dihedral anglesconsistent with those previously reported from the committor test analysis. Thepresent study offers an AI-aided framework to explain the appropriate reactioncoordinates, which acquires considerable significance when the number ofdegrees of freedom increases.

journal of chemical physics online/the journal of chemical physics/journal of chemical physics

Explaining reaction coordinates of alanine dipeptide isomerization  obtained from deep neural networks using Explainable Artificial Intelligence  (XAI)