Work

Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials

Year: 2025

Type: article

Abstract: Allowing atoms or molecules to disappear is a critical step in alchemical free energy simulations (FES). The necessary tricks are well understood when using force fields. Over the past few years, neural network potentials (NNPs) have seen rapid development. Their potentially higher accuracy compared to force fields makes them attractive for use in FES. Here, we outline a method for gradually decoupling atoms and molecules in systems that are fully described by NNPs. Specifically, we show that manipulating the neighbor list is equivalent to using soft-core potentials in force-field-based FES. Since constructing the neighbor list is a central step, regardless of the NNP's inner workings, our approach is agnostic to NNP architecture. We validate the correctness of our methodology by demonstrating cycle closure for a model problem and report solvation free energies obtained with the MACE-OFF23(S/M) NNP.

Source: The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c02980

Authors: Anna Katharina Picha, Sara Tkaczyk, Thierry Langer, Marcus Wieder, Stefan Boresch

Institutions: University of Vienna, Open Molecular Software Foundation

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funder: Universität Wien