Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

TL;DR

This paper presents a machine learning approach to accurately predict molecular atomization energies using nuclear charges and atomic positions, simplifying the quantum chemistry problem.

Contribution

It introduces a novel ML model that predicts atomization energies directly from molecular structure, achieving high accuracy and efficiency.

Findings

Mean absolute error of ~10 kcal/mol in predictions

Effective for predicting molecular potential energy curves

Comparable or better than traditional density-functional methods

Abstract

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schr\"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.