ML potentials can beat the methods they're trained on, new preprint argues
PavloDral · x · 2026-10-20
A Perspective preprint (arXiv:2610.18027, Dral et al.) challenges the consensus that machine learning potentials are accuracy-capped by their training methods. By generalizing across a third space — quantum-chemical methods — method-transferable MLPs predict at unseen methods and, in both density-functional and wavefunction-based implementations, can outperform any of their training methods non-empirically. The authors argue this will reshape training dataset construction and model evaluation, pushing accuracy limits of chemical simulations.
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