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Title: Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning

Journal Article · · Chem Catalysis
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
  1. University of Michigan, Ann Arbor, MI (United States); University of Michigan - Ann Arbor, MI (United States). Department of Chemical Engineering
  2. University of Michigan, Ann Arbor, MI (United States)
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Here, we show that unsupervised machine learning (ML) using principal component analysis (PCA) provides a straightforward pathway for developing accurate and interpretable electronic-structure descriptors of the chemical and catalytic properties of materials. We demonstrate the approach by finding chemisorption descriptors for metal alloys and surface oxygens on metals and metal oxides. In both cases, the principal component (PC) descriptors yield ML models that predict the material’s chemical properties with competitive accuracy compared to ML models built using established descriptors. Importantly, interpreting the electronic-structure patterns captured by each PC descriptor via signal reconstruction suggests potential design motifs for future electronic-structure descriptor design and allows us to identify links between a material’s geometric and catalytic properties. Ultimately, we show that the unsupervised ML approach provides a route to find electronic-structure descriptors of the catalytic properties of materials that readily connect to geometric structure and composition.

Research Organization:
University of Michigan, Ann Arbor, MI (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)
Grant/Contract Number:
AC02-05CH11231; FG02-05ER15686; SC0021008
OSTI ID:
2338173
Journal Information:
Chem Catalysis, Journal Name: Chem Catalysis Journal Issue: 4 Vol. 1; ISSN 2667-1093
Publisher:
Cell PressCopyright Statement
Country of Publication:
United States
Language:
English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
alloys
catalysis
chemisorption
computational chemistry
d-band model
data science
electronic structure
machine learning
principal-component analysis