PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.

ABSTRACT: Electrochemical energy storage and conversion play increasingly important roles in electrification and sustainable development across the globe. A key challenge therein is to understand, control, and design electrochemical energy materials with atomistic precision. This requires inputs from molecular modeling powered by machine learning (ML) techniques. In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along with PiNet2-dipole for dipole and charge predictions as well as PiNet2-χ for generating atom-condensed charge response kernels. By benchmarking publicly accessible data sets of small molecules, crystalline materials, and liquid electrolytes, we

SUBMITTER: Li J 

PROVIDER: S-EPMC11823406 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature