结合过渡路径采样与数据驱动的集体变量,通过反应性偏差的射击算法

Jintu Zhang1,2, Odin Zhang1, Luigi Bonati2

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.

概括

这项研究引入了一种新的机器学习算法,以增强计算机化学中的罕见事件采样. 该方法通过优化集体变量以更好地生成反应路径来提高过渡路径采样效率.

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