MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations.

ABSTRACT: The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol^-1. The models can be extrapolated to larger organic molecules with an average error of

SUBMITTER: Lee S 

PROVIDER: S-EPMC9214916 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature