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WIREs Comput Mol Sci
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Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear‐scaling relationships to statistical learning techniques

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Abstract The mechanistic analysis in heterogeneous catalysis is based on listing all elementary steps and evaluating explicitly their energies. To this end, computational models based on Density Functional Theory have become a standard to estimate the information needed in mechanistic studies. Typically, either the minimum energy paths or those with the smaller span are summarized in reaction profiles. Such simplifications gather a lot of information, although further dimensionality reduction is required to obtain the most relevant descriptors of catalytic activity and generate the so‐called volcano plots. The selection of descriptors has been traditionally based on simple intermediates, such as central atoms in small molecules (as C in CH4), which have good thermodynamic correlations to other fragments containing them. Yet, in emerging processes (recent studies), the number of intermediates involved increase, configurational effects and lateral interactions become significant, and complex materials with low symmetry are employed, thus the simple rules encapsulated in linear scaling relationships lose their predictive power due to error accumulation. At the same time, large datasets generated for the intermediates call for statistical analysis and thus these techniques are being leveraged to chemical systems, particularly to reduce their dimensionality. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Ab Initio Electronic Structure Methods
(a) Representation of the old workflows versus (b) the automated generation of reaction networks coupled with statistical post‐processing. In the first approach the experimental data was complemented by density functional energy calculations to obtain the reaction paths, and the linear‐scaling relationships between them. The result is the volcano plot. Alternatively, the new procedures automatically set up the calculations that are then stored in a database that are analyzed with the statistical approaches
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Schematic representation of dimensionality reduction techniques grouped as classifiers, feature selectors and pure dimensionality reductors: (a) 3 dimensional plot of f(x,y,z), where x and y are two normal randomly distributed variables, and z is a linear combination of x and y; (b) LASSO (c) random forest classifier, (d) t‐SNE, and (e) PCA applied on data illustrated in panel (a)
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Sources of complexity in heterogeneous catalysis at the levels of the material, the molecule, and the external factors
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Electronic Structure Theory > Ab Initio Electronic Structure Methods
Structure and Mechanism > Computational Materials Science
Structure and Mechanism > Reaction Mechanisms and Catalysis

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