论文标题
使用机器学习汉密尔顿人计算分子电动机屏障高度
Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights
论文作者
论文摘要
机器学习间的电位（MLIP）由于准确性和速度的结合而已成为计算化学家使用的常见工具。然而，目前尚不清楚这些工具在复杂分子中发现的状态状态下的表现如何。在这里，我们研究了MLIP在评估两个复杂的，分子运动系统的过渡屏障时的适用性：第一代Feringa电动机和9C烷烃第二代feringa电动机。我们比较了与层次相互作用的粒子神经网络（HIP-NN），PM3半经验量子法（SEQM），与HIP-NN（SEQM+HIP-NN）相连的PM3和密度功能理论计算的PM3。我们发现，使用SEQM+HIP-NN产生廉价，逼真的途径猜测，然后与DFT进行完善中间体，使我们能够廉价地找到逼真的反应路径和匹配实验的能源障碍，从而提供了证据，可以证明可以将深度学习用于高精度计算任务，例如过渡路径，同时还建议对高遍及的潜在应用进行高度的应用程序。
Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not clear how well these tools behave at or near transitions states found in complex molecules. Here we investigate the applicability of MLIPs in evaluating the transition barrier of two, complex, molecular motor systems: a 1st generation Feringa motor and the 9c alkene 2nd generation Feringa motor. We compared paths generated with the Hierarchically Interacting Particle Neural Network (HIP-NN), the PM3 semi-empirical quantum method (SEQM), PM3 interfaced with HIP-NN (SEQM+HIP-NN), and Density Functional Theory calculations. We found that using SEQM+HIP-NN to generate cheap, realistic pathway guesses then refining the intermediates with DFT allowed us to cheaply find realistic reaction paths and energy barriers matching experiment, providing evidence that deep learning can be used for high precision computational tasks such as transition path sampling while also suggesting potential application to high throughput screening.
