Machine Learning in Catalysis

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Machine learning models can help to reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, and overcome the shortcomings of linear scaling relations for more complex chemistries.

  • A. J. Chowdhury, W. Yang, K. E. Abdelfatah, M. Zare, A. Heyden, and G. A. Terejanu, “A multiple filter based neural network approach to the extrapolation of adsorption energies on metal surfaces for catalysis applications,” Journal of chemical theory and computation, vol. 16, iss. 2, pp. 1105-1114, 2020. doi:10.1021/acs.jctc.9b00986
    [BibTeX] [Download PDF]
    @article{Chowdhury_JCTC_2020,
    author = {Chowdhury, Asif J. and Yang, Wenqiang and Abdelfatah, Kareem E. and Zare, Mehdi and Heyden, Andreas and Terejanu, Gabriel A.},
    title = {A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications},
    journal = {Journal of Chemical Theory and Computation},
    volume = {16},
    number = {2},
    pages = {1105-1114},
    year = {2020},
    doi = {10.1021/acs.jctc.9b00986},
    note ={PMID: 31962041},
    URL = {https://doi.org/10.1021/acs.jctc.9b00986},
    eprint = {https://doi.org/10.1021/acs.jctc.9b00986}
    }

  • K. Abdelfatah, W. Yang, R. Vijay Solomon, B. Rajbanshi, A. Chowdhury, M. Zare, S. K. Kundu, A. Yonge, A. Heyden, and G. Terejanu, “Prediction of transition-state energies of hydrodeoxygenation reactions on transition-metal surfaces based on machine learning,” The journal of physical chemistry c, vol. 123, iss. 49, pp. 29804-29810, 2019. doi:10.1021/acs.jpcc.9b10507
    [BibTeX] [Download PDF]
    @article{AbdelfatahJ_JPCC_2019,
    author = {Abdelfatah, Kareem and Yang, Wenqiang and Vijay Solomon, Rajadurai and Rajbanshi, Biplab and Chowdhury, Asif and Zare, Mehdi and Kundu, Subrata Kumar and Yonge, Adam and Heyden, Andreas and Terejanu, Gabriel},
    title = {Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning},
    journal = {The Journal of Physical Chemistry C},
    volume = {123},
    number = {49},
    pages = {29804-29810},
    year = {2019},
    doi = {10.1021/acs.jpcc.9b10507},
    URL = {https://doi.org/10.1021/acs.jpcc.9b10507},
    eprint = {https://doi.org/10.1021/acs.jpcc.9b10507}
    }

  • A. J. Chowdhury, W. Yang, E. Walker, O. Mamun, A. Heyden, and G. A. Terejanu, “Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning,” The Journal of Physical Chemistry C, vol. 122, iss. 49, pp. 28142-28150, 2018. doi:10.1021/acs.jpcc.8b09284
    [BibTeX]
    @article{ChowdhuryJ_JPCC_2018,
    author = {Chowdhury, Asif J. and Yang, Wenqiang and Walker, Eric and Mamun, Osman and Heyden, Andreas and Terejanu, Gabriel A.},
    title = {{Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning}},
    journal = {{The Journal of Physical Chemistry C}},
    volume = {122},
    number = {49},
    pages = {28142-28150},
    year = {2018},
    doi = {10.1021/acs.jpcc.8b09284},
    }