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Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Pelletier, Vivienne, Bhat, Vedant, Rivera, Daniel J., Wilson, Steven A., Muhich, Christopher L.
Format:	Preprint
Publié:	2026
Sujets:	Chemical Physics Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

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