Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surf...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Hong Sun, Joshua A Vita, Amit Samanta, Vincenzo Lordi
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IOP Publishing 2026-01-01
Sarja:	Machine Learning: Science and Technology
Aiheet:	data mining graph autoencoder machine learning potentials
Linkit:	https://doi.org/10.1088/2632-2153/ae39a1
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