Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst

For disordered catalysts such as atomically dispersed “single-atom” metals on amorphous silica, the active sites inherit different properties from their quenched-disordered local environments. The observed kinetics are site-averages, typically dominated by a small fraction of highly active sites. St...

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Bibliographic Details
Published in:The Journal of chemical physics 2020-12, Vol.153 (24), p.244120-244120
Main Authors: Vandervelden, Craig A., Khan, Salman A., Peters, Baron
Format: Article
Language:English
Subjects:
Activation energy
Algorithms
Catalysts
Kinetics
Machine learning
Physics
Silicon dioxide
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Summary:For disordered catalysts such as atomically dispersed “single-atom” metals on amorphous silica, the active sites inherit different properties from their quenched-disordered local environments. The observed kinetics are site-averages, typically dominated by a small fraction of highly active sites. Standard sampling methods require expensive ab initio calculations at an intractable number of sites to converge on the site-averaged kinetics. We present a new method that efficiently estimates the site-averaged turnover frequency (TOF). The new estimator uses the same importance learning algorithm [Vandervelden et al., React. Chem. Eng. 5, 77 (2020)] that we previously used to compute the site-averaged activation energy. We demonstrate the method by computing the site-averaged TOF for a simple disordered lattice model of an amorphous catalyst. The results show that with the importance learning algorithm, the site-averaged TOF and activation energy can now be obtained concurrently with orders of magnitude reduction in required ab initio calculations.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0037450