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Updated: Jun 4, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Published on: January 26, 2024

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高精度机器学习原子间潜力的数据效率多忠实训练

Jaesun Kim1, Jisu Kim1, Jaehoon Kim1

  • 1Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Korea.

Journal of the American Chemical Society
|December 17, 2024
PubMed
概括

这项研究引入了一种机器学习的原子间潜能 (MLIP) 框架,该框架使用多真实数据库高效地学习精确的潜在能量表面. 该方法显著减少了对昂贵的高准确度数据的需求,提高了MLIP的准确性和适用性.

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