AI RESEARCH
QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
arXiv CS.LG
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ArXi:2605.10458v1 Announce Type: new Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as machine learning targets has been complicated by the absence of a principled out-of-distribution evaluation protocol at the atomic level. In this work, we propose a held-out evaluation protocol that clusters atomic environments by SOAP descriptors and computes metrics accounting only for cluster labels unseen during.