Rational Design and Investigation of Catalysts for Sustainable Resources

As a major part of trying to mitigate global climate change and improve sustainable resources, discovery of new, stable, and highly active catalyst materials is a pressing issue. There are several reasons for this, first, currently the most efficient and stable catalysts for the chemical processes that we use to transform raw resources into products with the desired functions (chemicals or energy), <i>e.g.</i>, H₂ evolution and CO₂ reduction, contain expensive rare and precious elements such as Pt and Ir. This explains the efforts to find abundant, accessible, low-cost, stable alternatives that will yield process efficiencies comparable or better than those we have today. For example, for water splitting, many new materials with different compositions have shown promising results as catalysts. However, they are mostly prepared by wet chemical synthesis, which results in chemical waste and can be too slow for industrial use. Second, the morphology of the catalysts is important because it affects their catalytic properties as higher surface areas yield more catalytically active sites, surface energetics change, leading to improved reaction rates, etc. These reasons emphasize the motivation to accelerate the process of finding new materials with varying nanostructures and optimized functionality, by systematic exploration of several parameter spaces.<br/>In recent years, artificial intelligence, namely by machine learning (ML) tools, has gained prominence in the field of materials science. The use of ML accelerates new material predictions and assists with finding unexpected correlations between the process-structure-function relations of materials, which leads to a better understanding and focus of the vast parameter spaces that exist in materials science. Rational design by ML in conjunction with combinatorial materials science promotes the rapid discovery and analysis of new materials, and enables breakthroughs in materials science, which would otherwise not have been possible.<br/>Here we present the progress in the development of electrocatalysts using rational design with ML in conjunction with combinatorial synthesis and high-throughput characterization. We investigate changes in composition and nano-morphology on material libraries and their effects on the catalysis of reactions such as O₂ evolution, CO₂ reduction, and CH₃OH oxidation. The different nanostructures and compositions show high activity and stability as electrocatalysts. The insights gained, indicate a dependence of catalytic activity on composition and nanostructuring, which the standard experimental techniques cannot achieve or explore, thus illustrating the importance and impact that composition and structure have, and will have, on developing sustainable catalysts. This can only be done by high-throughput experimentation design, combined with machine learning tools, which will assist with appropriate path directions and ensure rational studies on catalysis in the future.