Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations

TL;DR

This paper applies graph convolutional neural networks to classify molecular configurations, leveraging atomic forces encoded in graph vertices, to improve molecular optimization tasks which are traditionally NP-hard.

Contribution

It introduces a novel graph-convolutional approach for classifying molecular structures based on atomic forces, comparing two pooling methods for enhanced performance.

Findings

Effective classification of molecular configurations achieved

Graph pooling layers impact model performance

Potential for improved molecular optimization techniques

Abstract

Tackling molecular optimization problems using conventional computational methods is challenging, because the determination of the optimized configuration is known to be an NP-hard problem. Recently, there has been increasing interest in applying different deep-learning techniques to benchmark molecular optimization tasks. In this work, we implement a graph-convolutional method to classify molecular structures using the equilibrium and non-equilibrium configurations provided in the QM7-X data set. Atomic forces are encoded in graph vertices and the substantial suppression in the total force magnitude on the atoms in the optimized structure is learned for the graph classification task. We demonstrate the results using two different graph pooling layers and compare their respective performances.