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

TL;DR

This paper introduces a scalable reweighting framework for free energy profiles obtained from machine learning interatomic potentials, enabling accurate thermodynamic predictions across different models with reduced computational cost.

Contribution

The authors develop a systematic analytical reweighting method that corrects free energy profiles from a source MLIP to multiple target MLIPs, even with low phase-space overlap.

Findings

Successfully reweighted PMFs for a 601-atom Li$^+$ system across various DFT levels.

Achieved high-fidelity thermodynamic properties with significantly less computational effort.

Revealed clustering of MLIPs based on their training data influence on thermodynamics.

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.