An autoencoder that automatically generates an initial guess for the minimum energy pathway (MEP) calculations has been designed. Specifically, our autoencoder takes in the trajectories of molecular dynamics simulations as its input and facilitates the generation of feasible molecular coordinates. Two molecules (acetonitrile and alanine dipeptide) were tested using the nudged elastic band calculations and the results provided improvements over linear interpolation and image dependent pair potential methods in terms of the number of SCF iterations, demonstrating the utility of using an autoencoder type of an approach for MEP calculations.

中文翻译：

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基于深度学习的初步猜测，可进行最少的能量路径计算

设计了一种自动编码器，它可以自动生成最小能量路径（MEP）计算的初始猜测。具体来说，我们的自动编码器将分子动力学模拟的轨迹作为其输入，并有助于生成可行的分子坐标。使用微调的弹性带计算测试了两个分子（乙腈和丙氨酸二肽），结果在SCF迭代次数方面相对于线性插值法和图像依赖对势法提供了改进，证明了使用自动编码器类型的方法的实用性用于MEP计算。