Development of Machine Learning Models to Reproduce Coupled-Cluster Molecular Energies
Abstract
We attempted to reproduce molecular energies at the coupled-cluster level from molecular structures by constructing machine learning models that reproduce atomic environment energies, defined as the difference between the atomic energy in a molecule and that of the corresponding isolated atom. To obtain atomic energies from quantum chemical calculations, we adopted energy density analysis. Our results showed that the prediction accuracy of coupled-cluster molecular energies was comparable to that for density functional theory.
Fields of Study & Subjects
Advanced Chemical Physics Studies
Machine Learning in Materials Science
Computational Drug Discovery Methods
Publication details
- Journal / source
- Journal of Computer Chemistry Japan
- Volume
- 24
- Issue
- 3
- Pages
- 95–98
- Publication date
- 1 January 2026
- Language
- EN