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

Craig A. Vandervelden, Salman A. Khan, Baron Peters

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number244120
JournalJournal of Chemical Physics
Issue number24
StatePublished - Dec 28 2020

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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