Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building

2026-05-15

AI Summary

This work addresses the high computational cost of accurate free energy profiles—such as potential of mean force (PMF)—and the inconsistency of existing machine learning interatomic potentials (MLIPs) due to divergent training data, which renders conventional reweighting methods ineffective under low phase-space overlap. The authors propose a scalable PMF reweighting framework that leverages sampling from a single source MLIP and incorporates an analytical correction based on mean energy differences to robustly reconstruct high-fidelity free energy profiles for multiple target MLIPs, circumventing the statistical collapse inherent in exponential reweighting. Applied to a 601-atom Li⁺-confined electrolyte system, the method accurately reproduces reaction and activation free energies at various DFT levels (PBE+D3, PBE-sol, r²SCAN) at minimal cost, reveals natural clustering in MLIP training data, and establishes a protocol for cross-model thermodynamic consistency diagnostics without redundant simulations.

Abstract

Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a reaction coordinate, referred to as the potential of mean force (PMF), with density functional theory (DFT) accuracy is computationally expensive. Universal machine learning interatomic potentials (MLIPs) drastically reduce this cost, but their accuracy is strongly determined by their training data and hence can be uncertain for a given system. In this work, we present a systematic and scalable framework for reweighting PMFs, initially sampled with a single'source'MLIP, across a representative suite of target MLIPs. Because traditional direct exponential reweighting fails for large system sizes due to low phase-space overlap between potentials, we deploy robust analytical corrections. Applying this to a complex 601-atom system of Li$^+$ transport in a nanoconfined electrolyte, we demonstrate that a mean energy-gap approximation effectively bypasses statistical collapse, producing a highly stable PMF matching the target PMF. Using this approach, we recover high-fidelity target thermodynamics across multiple DFT reference levels (PBE+D3, PBE-sol, r$^2$SCAN,r$^2$SCAN-D4) at a fraction of the computational cost of full simulations. Furthermore, thermodynamic analysis reveals that the studied MLIPs partition into two distinct clusters driven by their training data. Our reweighting framework successfully recovers target thermodynamic properties--specifically, reaction and activation free energies--even when the phase-space overlap between potentials is critically low. Ultimately, this approach establishes a vital diagnostic protocol to achieve affordable cross-model consensus on materials chemistry properties without redundant, resource-intensive simulations.

Problem

• free energy profile
• machine learning interatomic potentials
• potential of mean force
• phase-space overlap
• thermodynamic consensus

Innovation

• free energy reweighting
• machine learning interatomic potentials
• potential of mean force
• phase-space overlap
• thermodynamic consensus