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q-pac: A Python Package for Machine Learned Charge Equilibration Models

Authors :
Martin Vondrák
Karsten Reuter
Johannes T. Margraf
Publication Year :
2023
Publisher :
American Chemical Society (ACS), 2023.

Abstract

Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They therefore lack a description of long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based charge equilibration models, which allow the rigorous calculation of long-range electrostatic interactions and the energetic response of molecules and materials to external fields. The recently reported kQEq method achieves this by predicting local atomic electronegativities using Kernel ML. This paper describes the q-pac Python package, which implements several algorithmic and methodological advances to kQEq and provides an extendable framework for the development of ML charge equilibration models.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........bbbf568c802e6375f4bbf56249264759
Full Text :
https://doi.org/10.26434/chemrxiv-2023-n0dxz