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arXiv Machine Learning By Xiaoyu Wang, Bingqing Cheng

Distilling latent electrostatics from foundation machine learning interatomic potentials

Read the original on arXiv Machine Learning →

arXiv:2606. 15001v1 Announce Type: cross Abstract: Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiting their use for systems governed by long-range interactions and electrical response.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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