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When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Gibson, Jason B., Janicki, Tesia D., Hire, Ajinkya C., Bishop, Chris, Lane, J. Matthew D., Hennig, Richard G.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Materials Science Machine Learning Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

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