Extracting Knowledge from Data through Catalysis Informatics

Abstract

Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced microkinetic models, and the development of open-source software tools. Ultimately, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

“…This study presents not only the advantages of ML but also the limitations and difficulties of ML for heterogeneous catalysis. The schemes proposed and results obtained provide a valuable contribution to establishing “catalysis Informatics.”…”

A Disquisition on the Active Sites of Heterogeneous Catalysts for Electrochemical Reduction of CO2 to Value‐Added Chemicals and Fuel

“…All this requires high level understanding of stable crystal structures with exposed facets/terminations containing sites favoring CO adsorption and corresponding energetics. Numerous efforts are conducted to obtain direct insights into the energetics of active sites and bond‐breaking events by DFT with atomic‐scale structure inferred experimentally . While the technique offers reasonable accuracy stipulated by chosen postulations, it is severely bottlenecked by high computational costs and time consuming one‐at‐a‐time calculations.…”

“…Numerous efforts are conducted to obtain direct insights into the energetics of active sites and bond-breaking events by DFT with atomic-scale structure inferred experimentally. [344] While the technique offers reasonable accuracy stipulated by chosen postulations, it is severely bottlenecked by high computational costs and time consuming one-at-a-time calculations. To address these bottlenecks, ML in concert with first-principles calculations and experiments can drive forward rational catalyst designs and recent developments in this area is discussed in this section.…”

Formation and Functioning of Bimetallic Nanocatalysts: The Power of X‐ray Probes

“…[48] In X-ray spectroscopy,f or example,m odulation excitation spectroscopy coupled with phase sensitive detection (MES-PSD) increases the signal sensitivity towards the active part of the catalyst, for example, the NP surface in reaction, while filtering out the spectator contribution. [48] In X-ray spectroscopy,f or example,m odulation excitation spectroscopy coupled with phase sensitive detection (MES-PSD) increases the signal sensitivity towards the active part of the catalyst, for example, the NP surface in reaction, while filtering out the spectator contribution.…”

“…As size and information content of datasets increase, smart, often (semi-)automated aposteriori data treatment methods can replace classic time-expensive and comparatively inaccurate analysis. [48] In X-ray spectroscopy,f or example,m odulation excitation spectroscopy coupled with phase sensitive detection (MES-PSD) increases the signal sensitivity towards the active part of the catalyst, for example, the NP surface in reaction, while filtering out the spectator contribution. [49] Wavelet-transformed (WT) XAS can simultaneously determine the atom type (k-space) and location (Rspace) of the X-ray absorbersn eighbors,i nc ontrast to kspace-blind Fourier transformed (FT) EXAFS.…”