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Computational Catalyst Discovery: Active Classification Through Myopic Multiscale Sampling
  • Kevin Tran, Carnegie Mellon University
  • Willie Neiswanger, Carnegie Mellon University
  • Kirby Broderick, Carnegie Mellon University
  • Eric Xing, Carnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence
  • Jeff Schneider, Carnegie Mellon University
  • Zachary W. Ulissi, Carnegie Mellon University
Document Type

The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a "best"catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?"Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as "worth investigating"or "not worth investigating"experimentally. Our results show an ∼7-16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

Publication Date

Preprint: arXiv

  • Archived with thanks to arXiv
  • Preprint license: CC by 4.0
  • Uploaded 30 March 2022
Citation Information
K. Tran, W. Neiswanger, K. Broderick, E. Xing, J. Schneider, and Z. W. Ulissi, "Computational catalyst discovery: Active classification through myopic multiscale sampling," 2021, arXiv:2102.01528.