EAC-qm: Code and Models for Atomic Charges and Local Dipoles from Machine-Learned Charge Densities
Abstract
This repository provides a curated snapshot of the EAC-Net framework and associated resources used in the study of "Atomic Charges and Local Dipoles from Machine-Learned Charge Densities: A Consistent Description of Substituent Effects". The purpose of this deposit is to ensure methodological transparency, versioned reproducibility, and long-term accessibility of the models and scripts used in the accompanying manuscript.
Fields of Study & Subjects
Advanced Chemical Physics Studies
Machine Learning in Materials Science
Computational Drug Discovery Methods
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 27 January 2026