用神经网络进行瞬时速率计算的势能曲面插值

April M. Cooper, Philipp P. Hallmen, J. Kastner
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引用次数: 20

摘要

利用人工神经网络拟合势能面。我们证明了不仅使用能量,而且使用它们的一阶和二阶导数作为神经网络的训练数据的好处。这确保了平滑和精确的黑森表面,这是使用瞬子理论计算速率常数所必需的。我们的目标是一个局部的、精确的拟合,而不是一个全局的PES,因为瞬子理论只需要在主隧道路径附近的势能信息。在过渡态沿振型的伸长被用作神经网络的坐标。将该方法应用于甲醇的抽氢反应,并在理论的耦合簇水平上进行了计算。这个反应在天体化学中是解释星际介质中甲醇氘化的关键。
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Potential energy surface interpolation with neural networks for instanton rate calculations
Artificial neural networks are used to fit a potential energy surface. We demonstrate the benefits of using not only energies, but also their first and second derivatives as training data for the neural network. This ensures smooth and accurate Hessian surfaces, which are required for rate constant calculations using instanton theory. Our aim was a local, accurate fit rather than a global PES, because instanton theory requires information on the potential only in the close vicinity of the main tunneling path. Elongations along vibrational normal modes at the transition state are used as coordinates for the neural network. The method is applied to the hydrogen abstraction reaction from methanol, calculated on a coupled-cluster level of theory. The reaction is essential in astrochemistry to explain the deuteration of methanol in the interstellar medium.
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