Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States,Topics in Catalysis

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Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States
Topics in Catalysis ( IF 2.8 ) Pub Date : 2021-09-12 , DOI: 10.1007/s11244-021-01506-0
Shusen Chen ₁ , Taylor Nielson ₁ , Elayna Zalit ₁ , Braden Borough ₁ , William J. Hirschi ₁ , Spencer Yu ₁ , Daniel H. Ess ₁ , Bastian Bjerkem Skjelstad ₂ , David Balcells ₃

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Quantum–mechanical transition states can aid in the identification of promising catalysts for methane C–H activation and functionalization. However, only a limited amount of the vast metal–ligand chemical space has been computationally evaluated. To begin to solve this problem, we showcase a workflow that combines automated construction of Pt(II)-ligand combinations and automated transition-state searching with machine learning to maximize the generation of fully optimized transition states.