Computational catalyst discovery: Active classification through myopic multiscale sampling

TL;DR

This paper introduces a new active classification method called myopic multiscale sampling for computational catalyst discovery, which efficiently identifies promising catalysts by combining multiscale modeling and automated DFT calculation selection, significantly speeding up the process.

Contribution

It presents a novel active classification approach that balances exploration and exploitation in catalyst discovery using multiscale models and automated DFT sampling.

Findings

Achieved 7-16 times faster catalyst classification compared to random sampling.

Validated the method on both synthetic and real datasets.

Demonstrated effective classification of catalysts as worth investigating.

Abstract

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 straight-forward when using ensembles of atomic-scale calculations (e.g., density functional theory). We attempt to address this issue by creating a multiscale model that estimates bulk catalyst activity using adsorption energy predictions from both density functional theory 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 density functional theory calculations. It is an active classification strategy that seeks to classify catalysts as "worth investigating" or "not worth investigating" experimentally. Our results show a ~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.