“Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors

Journal Article · Fri Sep 27 00:00:00 EDT 2019 · ACS Catalysis

DOI:https://doi.org/10.1021/acscatal.9b03599· OSTI ID:1595081

Timoshenko, Janis ^[1]; Frenkel, Anatoly I.  ^[2]

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1. Max Planck Society, Berlin (Germany). Fritz-Haber-Inst., Dept. of Interface Science;
2. Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

The rapid growth of methods emerging in the past decade for synthesis of “designer” catalysts—ranging from the size and shape-selected nanoparticles to mass-selected clusters, to precisely engineered bimetallic surfaces, to single site and pair site catalysts—has opened opportunities for tailoring the catalyst structure for the desired activity and selectivity. It has also sharpened the need for developing approaches to the operando characterization, ones that identify the catalytic active sites and follow their evolutions in reaction conditions. Commonly used methods for determination of the activity descriptors in the nanocatalysts, based on the correlation between the changes in catalyst performance and evolution of its structural and electronic properties, are hampered by the paucity of experimental techniques that can detect such properties with high accuracy and in reaction conditions. Out of many such techniques, X-ray absorption spectroscopy (XAS) stands out as an element-specific method that is very sensitive to the local geometric and electronic properties of the metal atoms and their surroundings and, therefore, is able to track catalyst structure modifications in operando conditions. Despite the vast amount of structure-specific information (such as, e.g., the charge states and radial distribution function of neighbors of selected atomic species) stored in the XAS data of catalysts, extracting it from the spectra is challenging, especially in the conditions of low metal weight loading, nanoscale dimensions, heterogeneous size and composition distributions, and harsh reaction environment. In this Perspective, we discuss the recent developments in XAS data analysis achieved by employing supervised and unsupervised machine learning (ML) methods for structural characterization of catalysts. By benefiting from the sensitivity of ML methods to subtle variations in experimental data, a previously “hidden” relationship between the X-ray absorption spectrum and descriptors of material’s structure and/or composition can be found, as illustrated on representative examples of mono-, hetero-, and nonmetallic catalysts. In the case of supervised ML, the experimental spectra can be rapidly “inverted”, and the structure of the catalyst can be tracked in real time and in reaction conditions. Finally, emerging opportunities for catalysis research that the ML methods enable, such as high-throughput data analysis, and their applications to other experimental probes of catalyst structure are discussed.

• Research Organization: Energy Frontier Research Centers (EFRC) (United States). Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC); Harvard Univ., Cambridge, MA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES); European Research Council (ERC)
• Grant/Contract Number: SC0012573; SC0012704; FG02-03ER15476
• OSTI ID: 1595081
• Journal Information: ACS Catalysis, Vol. 9, Issue 11; ISSN 2155-5435
• Publisher: American Chemical Society (ACS)
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY