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Updated: Sep 9, 2025

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
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Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

482

反応性アクティブ・ラーニング: 反応系における機械学習の効率的なアプローチ

Siddarth K Achar1, Priyanka B Shukla2, Chinmay V Mhatre2

  • 1Computational Modeling & Simulation Program, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.

Journal of chemical theory and computation
|September 3, 2025
PubMed
まとめ

新しい反応性アクティブラーニング (RAL) フレームワークは,複雑な化学反応のための機械学習の原子間ポテンシャル (MLIP) を効率的に訓練します. このアプローチは,反応経路の正確な予測と新しい触媒の発見を可能にし,従来の方法の限界を克服します.

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