Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Abstract

The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in data science could have a great impact on traditional catalysis research in both industry and academia and could accelerate the development of catalysts. Machine learning (ML), a subfield of data science, can play a central role in this paradigm shift away from the use of traditional approaches. In this review, we present a user’s guide for ML that we believe will be helpful for scientists performing research in the field of catalysis and summarize recent progress that has been made in utilizing ML to create homogeneous and heterogeneous catalysts. The focus of the review is on the design, synthesis, and characterization of catalytic materials/compounds as well as their applications to catalyzed processes. The ML technique not only enhances ways to discover catalysts but also serves as a powerful tool to establish a deeper understanding of relationships between the properties of materials/compounds and their catalytic activities, selectivities, and stabilities. This knowledge facilitates the establishment of principles employed to design catalysts and to enhance their efficiencies. Despite such advantages of ML, it is noteworthly that the current ML-assisted development of real catalysts remains in its infancy, mainly because of the complexity of catalysis associated with the fact that catalysis is a time-dependent dynamic event. In this review, we discuss how seamless integration of experiment, theory, and data science can be used to accelerate catalyst development and to guide future studies aimed at applications that will impact society’s need to produce energy, materials, and chemicals. Moreover, the limitations and difficulties of ML in catalysis research originating from the complex nature of catalysis are discussed in order to make the catalysis community aware of challenges that need to be addressed for effective and practical use of ML in the field.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

“…For example, the calculated gauge-independent 13 Step 6. Using the foregoing ISPCA model 1, "2 nd generation" catalysts [15][16][17][18][19][20][21][22] were predicted and synthesized ( Figure 5), focusing on changing characteristics of the catalyst in accordance with the relative weights of the descriptors in the model. Prospective test catalysts were first evaluated using the "molecular ruler" approach wherein, based on ISPCA model 1, a good catalyst must be 1.7 units in PCA space (in any direction) from the nearest training set neighbor in order to expect a substantial change in selectivity (D selectivity = 0.46 or 1 s).…”

“…Catalyst 17, containing both a sterically encumbered pyridine (correlated to free pyridine o- 13 C NMR shift and imido-Ti-Npy angle) and poorly donating iodide ligands was predicted (3.65) and experimentally confirmed (2.41) to be more selective than any catalyst in the training set. 17 is a good example of potential additive effects on selectivity: 16, which has stronger Ti-Cl bonds (and is thus less Lewis acidic than 17), and 10, which is similarly Lewis acidic but lacks steric encumbrance on pyridine, each have worse selectivity.…”

“…2,13 Modern approaches have directed significant effort at computer-aided design of selective catalysts, [14][15][16] however, the field of catalysis informatics is still immature. 17,18 In recent years, machine learning and multivariate regression analysis have emerged as powerful tools for detailed modeling and prediction in chemical catalysis. Machine learning uses a "top-down" approach where correlations are developed through complex neural networks.…”

Single-atom site catalysts for environmental catalysis

“…Machine learning (ML), is a subfield of data science with a central role in the paradigm shift from the use of traditional approaches. ML is a powerful approach to discover and design novel catalysts through the establishment of deeper relationships between the structure/activity relationship and principles of catalysts [151]. Well performing SACs during reaction processes all have something in common, such as Pt-based materials for CO oxidation, Cu-SACs for electrocatalysis of CO2 to C2, and M-Nx/Cfor oxygen reduction reaction.…”

Recent Progress in the Development of Screening Methods to Identify Electrode Materials for the Oxygen Evolution Reaction

“…[100] The motivated data-mining activities in conjunction with machine-learning techniques may spur further progress in computational electrocatalysis, as also evident from the recent literature. [101][102][103] A pressing issue in electrocatalysis, particularly for the OER in an acidic electrolyte, corresponds to the aspect of catalyst stability. Regrettably, the most active OER materials, such as RuO 2 , lack long-term stability, restricting their use for practical applications.…”