EAC-qm: Code and Models for Atomic Charges and Local Dipoles from Machine-Learned Charge Densities

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Authors: Xuejian Qin, Taoyuze Lv, Zhicheng Zhong
Cited by: 0

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