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Updated: Feb 13, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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機械学習のための長距離静電学 原子間ポテンシャル 思ったより簡単です

Dongjin Kim1, Bingqing Cheng1,2,3,4

  • 1Department of Chemistry, UC Berkeley, Berkeley, California 94720, USA.

The Journal of chemical physics
|February 12, 2026
PubMed
まとめ

現代の機械学習の原子間ポテンシャル (MLIP) には,遠距離静電学が欠けている. Latent Ewald Summationのフレームワークは,環境依存の電荷を用いてこれらの相互作用を捉え,曖昧な部分電荷を回避します.

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