This research uses a compressed-sensing data-analytics approach parameterized with density-functional inputs to address the challenge of discovering new single-atom alloy catalysts (SAACs). The method predicts the catalytic properties of SAACs efficiently, identifying over 200 promising, previously unreported candidates, some superior to known SAACs. A novel subgroup discovery method analyzes the complex models, highlighting the importance of data analytics in unbiased catalyst design.
Publisher
Nature Communications
Published On
Mar 23, 2021
Authors
Zhong-Kang Han, Debalaya Sarker, Runhai Ouyang, Aliaksei Mazheika, Yi Gao, Sergey V. Levchenko
Tags
single-atom alloy catalysts
compressed-sensing
data analytics
catalytic properties
subgroup discovery
catalyst design
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