论文标题
势能表面与神经网络的插值进行插入式计算
Potential energy surface interpolation with neural networks for instanton rate calculations
论文作者
论文摘要
人工神经网络用于拟合势能表面。我们证明了不仅使用能量，还可以证明其第一和第二个衍生物作为神经网络的培训数据。这样可以确保光滑，准确的Hessian表面，这是使用Instanton理论进行速率恒定计算所必需的。我们的目标是局部，准确的拟合，而不是全球PE，因为Instanton理论仅需要在主要隧道路径附近的潜力信息。过渡状态下沿振动正常模式的伸长来用作神经网络的坐标。该方法应用于甲醇的氢抽象反应，该反应在理论的耦合群集水平上计算出来。该反应在星体化学中至关重要，以解释星际培养基中甲醇的剥离。
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.
