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As quantum chemical properties have a dependence on their geometries, graphneural networks (GNNs) using 3D geometric information have achieved highprediction accuracy in many tasks. However, they often require 3D geometriesobtained from high-level quantum mechanical calculations, which are practicallyinfeasible, limiting their applicability to real-world problems. To tacklethis, we propose a new training framework, GeoTMI, that employs denoisingprocess to predict properties accurately using easy-to-obtain geometries(corrupted versions of correct geometries, such as those obtained fromlow-level calculations). Our starting point was the idea that the correctgeometry is the best description of the target property. Hence, to incorporateinformation of the correct, GeoTMI aims to maximize mutual information betweenthree variables: the correct and the corrupted geometries and the property.GeoTMI also explicitly updates the corrupted input to approach the correctgeometry as it passes through the GNN layers, contributing to more effectivedenoising. We investigated the performance of the proposed method using 3D GNNsfor three prediction tasks: molecular properties, a chemical reaction property,and relaxed energy in a heterogeneous catalytic system. Our results showedconsistent improvements in accuracy across various tasks, demonstrating theeffectiveness and robustness of GeoTMI.

GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising