Neural Network Surrogates for Free Energy Computation of Complex Chemical Systems
Abstract
Free energy reconstruction methods such as Gaussian Process Regression (GPR) require Jacobians of the collective variables (CVs), a bottleneck that restricts the use of complex or machine-learned CVs. We introduce a neural network surrogate framework that learns CVs directly from Cartesian coordinates and uses automatic differentiation to provide Jacobians, bypassing analytical forms. On an MgCl2 ion-pairing system, our method achieved high accuracy for both a simple distance CV and a complex coordination-number CV. Moreover, Jacobian errors also followed a near-Gaussian distribution, making them suitable for GPR pipelines. This framework enables gradient-based free energy methods to incorporate complex and machine-learned CVs, broadening the scope of biochemistry and materials simulations.
Keywords
Cite
@article{arxiv.2510.01396,
title = {Neural Network Surrogates for Free Energy Computation of Complex Chemical Systems},
author = {Wasut Pornpatcharapong},
journal= {arXiv preprint arXiv:2510.01396},
year = {2026}
}
Comments
6 pages, 4 figures. This work has already been accepted for presentation in The 29th International Computer Science and Engineering Conference (ICSEC) 2025, Chiang Mai, Thailand, and will be published in IEEE Xplore