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

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Abstract

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.

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Data Availability

The data that supports the findings of this study are available within the article and its supplementary material. Mason and Taylor programs used in this work are available at https://github.com/DanielEss-lab/

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Acknowledgements

We thank Brigham Young University and the Office of Research Computing. This work was entirely supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Catalysis Science Program, under Award # DE-SC0018329 (to D.H.E). D. B. acknowledges the support from the Research Council of Norway through its Centers of Excellence Scheme (Project Number 262695) and the Norwegian Supercomputing Program (NOTUR; Project Number NN4654K). B. B. S. is supported by a scholarship from the Otsuka Toshimi Scholarship Foundation.

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Authors and Affiliations

  1. Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, 84602, USA

    Shusen Chen, Taylor Nielson, Elayna Zalit, Braden Borough, William J. Hirschi, Spencer Yu & Daniel H. Ess

  2. Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, 060-8628, Japan

    Bastian Bjerkem Skjelstad

  3. Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, 0315, Oslo, Norway

    David Balcells

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  1. Shusen Chen
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Chen, S., Nielson, T., Zalit, E. et al. Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States. Top Catal 65, 312–324 (2022). https://doi.org/10.1007/s11244-021-01506-0

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