Beyond Adam: disentangling optimizer effects in the fine-tuning of atomistic foundation models

Atomistic foundation models constitute a paradigm shift in computational materials science by providing universal machine-learned interatomic potentials with broad transferability across chemical spaces. Although fine-tuning is essential for adapting these pretrained models to specific target system...

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Váldodahkkit:	Xiaoqing Liu, Yangshuai Wang, Teng Zhao
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	IOP Publishing 2026-01-01
Ráidu:	AI for Science
Fáttát:	machine-learned interatomic potentials fine-tuning atomistic foundation models optimization algorithms
Liŋkkat:	https://doi.org/10.1088/3050-287X/ae5078
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