Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training methods, with improvements beyond this limit achieved so far only by refitting to experimental observables. In this Perspective, we argue that MLPs can also be made more accurate than their training methods non-empirically by generalizing across a third space, that of quantum-chemical methods. We propose routes to method-transferable MLPs that predict at unseen methods, with working implementations in both density-functional and wavefunction-based theories showing that they can be more accurate than any of their tra...
| Authors | Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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