Mapping out reaction pathways and their corresponding activation barriers is a significant aspect of molecular simulation. Given their inherent complexity and nonlinearity, even generating a initial guess of these paths remains a challenging problem. Presented in this paper is an innovative approach that utilizes neural networks to generate initial guess for these reaction pathways. The proposed method is initiated by inputting the coordinates of the initial state, followed by progressive alterations to its structure. This iterative process culminates in the generation of the approximate representation of the reaction path and the coordinates of the final state. The application of this method extends to complex reaction pathways illustrated by organic reactions. Training was executed on the Transition1x dataset, an organic reaction pathway dataset. The results revealed generation of reactions that bore substantial similarities with the corresponding test data. The method's flexibility allows for reactions to be generated either to conform to predetermined conditions or in a randomized manner.

翻译：映射反应路径及其对应的活化能垒是分子模拟中的一个重要方面。由于反应路径固有的复杂性和非线性，即便生成这些路径的初始猜测仍是一个具有挑战性的问题。本文提出了一种创新方法，利用神经网络生成这些反应路径的初始猜测。该方法首先输入初始态的坐标，然后逐步改变其结构，这一迭代过程最终生成反应路径的近似表示以及终态坐标。该方法可应用于有机反应所展示的复杂反应路径。模型使用有机反应路径数据集Transition1x进行训练，结果显示生成的产物与相应测试数据具有高度相似性。该方法的灵活性使其能够根据预设条件或随机方式生成反应。