A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes

Junmin Chen, Peichen Zhong

Submission Type: I want my submission to be considered for both oral and poster presentation.

Keywords: Electrolyte; Atomistic simulations; Force Field

TL;DR: A hybrid physics/ML force field for battery electrolytes trains only on monomer and dimer data, yet generalizes to bulk-phase property prediction with state-of-the-art accuracy and broad chemical space coverage.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 1st 2025, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1021/acs.jctc.5c02100

Submission Number: 435