论文标题
通过交叉透镜最小化学习反应坐标：应用于丙氨酸二肽
Learning reaction coordinates via cross-entropy minimization: Application to alanine dipeptide
论文作者
论文摘要
我们提出了一种跨凝性最小化方法，用于从复杂分子系统中的大量集体变量中找到反应坐标。此方法是用乙状结肠描述委员会功能的可能性最大化方法的扩展。通过设计，对反应坐标作为各种集体变量的函数进行了优化，以便可以以sigmoidal的方式描述委员会$ p_ \ mathrm {b}^*$值的分布。我们还介绍了机器学习字段中使用的$ L_2 $ norm正则化，以防止当考虑的集体变量数量较大时过度拟合。当前方法用于研究真空中丙氨酸二肽的异构化，其中45个二面角用作候选变量。正则化参数是通过使用训练和测试数据集的交叉验证来确定的。证明最佳反应坐标涉及重要的二面角，这与先前报道的结果一致。此外，具有$ p_ \ mathrm {b}^*\ sim 0.5 $的点清楚地表明了使用提取的二面角的分子区分反应物和乘积状态在平均力的潜力上。
We propose a cross-entropy minimization method for finding the reaction coordinate from a large number of collective variables in complex molecular systems. This method is an extension of the likelihood maximization approach describing the committor function with a sigmoid. By design, the reaction coordinate as a function of various collective variables is optimized such that the distribution of the committor $p_\mathrm{B}^*$ values generated from molecular dynamics simulations can be described in a sigmoidal manner. We also introduce the $L_2$-norm regularization used in the machine learning field to prevent overfitting when the number of considered collective variables is large. The current method is applied to study the isomerization of alanine dipeptide in vacuum, where 45 dihedral angles are used as candidate variables. The regularization parameter is determined by cross-validation using training and test datasets. It is demonstrated that the optimal reaction coordinate involves important dihedral angles, which are consistent with the previously reported results. Furthermore, the points with $p_\mathrm{B}^*\sim 0.5$ clearly indicate a separatrix distinguishing reactant and product states on the potential of mean force using the extracted dihedral angles.
