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Updated: Feb 14, 2026

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機械学習の体順序パラドックスを解く 原子間ポテンシャル

Sanggyu Chong1, Tong Jiang2, Michelangelo Domina1

  • 1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.

The Journal of chemical physics
|February 13, 2026
PubMed
まとめ

機械学習の原子間潜在力 (MLIP) は,暗黙的にエネルギー貢献を学習する. この研究は,MLIPが独自の身体秩序の傾向を発展させ,正確性と一般化性に影響を与えることを明らかにしています.

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