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arXiv AI By Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen, Hassan Nawaz, Mateusz Witkowski, Szymon \'Smiga, Ireneusz Grabowski

Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?

Read the original on arXiv AI →

The article discusses how machine learning potentials (MLPs) can surpass the accuracy of the quantum‑chemical methods they are trained on by generalizing across a third space—different quantum‑chemical methods. It proposes the development of method‑transferable MLPs that predict properties at unseen methods, demonstrating that such models can outperform any of their training methods in both density‑functional and wavefunction‑based theories. The authors argue that pursuing accuracy beyond the training methods could enable universal MLPs to exceed single‑method models, influencing dataset construction and model evaluation to push chemical simulation limits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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