Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks

TL;DR

This paper introduces a novel approach that combines Coulomb matrix representations with atomic composition features in a Bayesian neural network to improve the prediction accuracy of molecular atomization energies.

Contribution

It demonstrates that integrating molecular geometry and atomic composition features enhances the prediction of electronic properties over previous methods.

Findings

Reduced mean absolute error from 3.51 to 3.0 kcal/mol on QM7 dataset

Combining features improves prediction accuracy

Bayesian regularized neural networks effectively model molecular energies

Abstract

Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol.