随着承诺者流:一个数据驱动的过渡途径的发现
Cheng Giuseppe Chen1, Chenyu Tang1, Alberto Megías2
1Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Université de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France.
Journal of chemical theory and computation
|January 16, 2026
概括
这项研究引入了一种新的代框架,以准确识别分子过渡途径并估计反应速率. 该方法通过使用神经网络来改进犯人概率,以进行增强的分子动态模拟.
科学领域:
- 计算化学的计算化学
- 分子动力学分子动力学
- 反应机制的发现反应机制的发现
背景情况:
- 在分子系统中识别罕见事件和过渡途径仍然是一个重大挑战.
- 提交者概率是分子模拟中定义反应坐标的关键指标.
- 确保过渡路径和提交函数之间的一致性对于机械准确性至关重要.
研究的目的:
- 开发一个代框架来推断犯人概率.
- 确定和完善分子系统中最相关的过渡途径.
- 为了能够准确地估计反应速率常数.
主要方法:
- 一种使用偏向抽样和神经网络近似进行犯人概率的代方法.
- 从已学到的委托器同委托器表面提取主导过渡通道.
- 代改进提交者和过渡路径,直到趋同.
主要成果:
- 拟议的框架成功地推断了犯人概率,并确定了关键的过渡途径.
- 在基准系统上证明有效性:2D模型潜力,过渡,迪尔斯-阿尔德反应和Trp折叠.
- 精细的提交器可以准确地估计反应速率常数.
结论:
- 代框架为发现过渡途径和理解反应机制提供了一个强大的方法.
- 这种方法通过提高犯人估计的准确性来增强分子动力学模拟.
- 该方法在研究分子系统中罕见事件方面取得了重大进展.
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