Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
J Chem Theory Comput. 2022 Jul 12;18(7):4282-4292. doi: 10.1021/acs.jctc.2c00331. Epub 2022 Jun 23.
Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimizations on the fly, and exploit its good performance and transferability in identifying geometry optimization failures for catalyst design. We show that the dynamic classifier performs well on all reactive intermediates in the representative catalytic cycle of the radical rebound mechanism for the conversion of methane to methanol despite being trained on only one reactive intermediate. The dynamic classifier also generalizes to chemically distinct intermediates and metal centers absent from the training data without loss of accuracy or model confidence. We rationalize this superior model transferability as arising from the use of electronic structure and geometric information generated on-the-fly from density functional theory calculations and the convolutional layer in the dynamic classifier. When used in combination with uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered.
虚拟高通量筛选（VHTS）和机器学习（ML）极大地加速了单中心过渡金属催化剂的设计。然而，由于难以同时将所有与反应机理相关的反应中间体收敛到预期的几何形状和电子态，催化剂的 VHTS 往往伴随着高计算失败率和浪费计算资源。我们展示了一种动态分类器方法，即卷积神经网络，它可以实时监测几何优化，并利用其在识别催化剂设计中几何优化失败方面的良好性能和可转移性。我们表明，尽管仅在一个反应中间体上进行了训练，但动态分类器在甲烷转化为甲醇的自由基回弹机制的代表性催化循环中的所有反应中间体上都表现出良好的性能。动态分类器还可以推广到化学性质不同的中间体和训练数据中不存在的金属中心，而不会降低准确性或模型置信度。我们将这种优越的模型可转移性归因于从密度泛函理论计算和动态分类器中的卷积层实时生成的电子结构和几何信息的使用。当与不确定性量化结合使用时，动态分类器为所有正在考虑的反应中间体节省了一半以上的计算资源，这些计算资源本来会因不成功的计算而浪费。
