基于神经网络的势能面插值计算瞬子速率
Potential energy surface interpolation with neural networks for instanton rate calculations
摘要：
人工神经网络用于拟合势能面。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。这确保了使用瞬子理论计算速率常数所需的平滑和准确的海森表面。我们的目标是一个局部的，准确的拟合，而不是一个整体的PES，因为瞬子理论只需要在主隧道path.Elongation附近的潜在信息沿振动正常模式的过渡态被用作神经网络的坐标。该方法适用于从甲醇中提取氢的反应，计算的耦合集群水平的理论。该反应在天体化学中是解释星际介质中甲醇氘化的必要条件。
英文摘要：
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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