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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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Published on: April 26, 2024

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反应式主动学习:用于训练机器学习的有效方法 反应系统的原子间潜力

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
概括

一个新的反应式主动学习 (RAL) 框架有效地训练复杂化学反应的机器学习原子间潜力 (MLIP). 这种方法可以准确预测反应路径并发现新型催化剂,克服传统方法的局限性.

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