Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
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 MLI...
| Authors | Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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