aiqbench analysis
This research demonstrates that machine learning potentials can generalize across quantum-chemical methods to achieve accuracy surpassing their specific training data, challenging the conventional belief that model performance is capped by its underlying physics engine. By treating different quantum chemical theories as a distinct dimension for generalization, this approach offers a non-empirical pathway to push the limits of chemical simulations without relying on experimental refitting. The findings will significantly influence how datasets are constructed and models are evaluated in computational chemistry.
Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
arXiv:2610.18027v1 Announce Type: cross Abstract: 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 training methods. Aiming beyond the accuracy of the training methods could also make universal MLPs more accurate than single-method models, even when the method-transferable MLPs remain less accurate than those QM methods. Method-transferable MLPs will shape the construction of training datasets and the evaluation of models, pushing the accuracy limits of chemical simulations.
Title:Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Abstract: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 training methods. Aiming beyond the accuracy of the training methods could also make universal MLPs more accurate than single-method models, even when the method-transferable MLPs remain less accurate than those QM methods. Method-transferable MLPs will shape the construction of training datasets and the evaluation of models, pushing the accuracy limits of chemical simulations.
Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
arXiv:2610.18027 [physics.chem-ph]
(or arXiv:2610.18027v1 [physics.chem-ph] for this version)
https://doi.org/10.48550/arXiv.2610.18027 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 267 words
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