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.

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

References in corpus (3)

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• A Method for Representing Periodic Functions and Enforcing Exactly Periodic Boundary Conditions with Deep Neural Networks
• Learning Free Energy Landscapes Using Artificial Neural Networks