From Computational to Data-Driven Strategies for Single-Atom Catalysis

The rising standards of living have significantly increased global energy demands and CO₂ emissions, strongly demanding new advancements in resource efficiency and sustainability. In this context, single-atom catalysts (SACs) offer a promising avenue toward unprecedented resource efficiency by replacing the large number of metal atoms required by traditional catalysts with just a few, thereby significantly reducing material cost and environmental footprint. However, regarding the understanding and optimization of SACs, their inherent heterogeneity makes the identification of active sites challenging. Consequently, theoretical explorations typically rely on single-speciation models that offer plausible but incomplete representations of SACs. This limitation underscores the need for integrated approaches that combine theoretical models with experimental data to streamline the development of the field.

In this thesis, Density Functional Theory (DFT) simulations are integrated with experimental synthesis, characterization, and catalytic testing techniques to explore the synthesis-structure-property relationships in SACs for different catalytic strategies. These findings underscore the critical influence of often neglected factors such as nuclearity, host environment, and synergistic interactions on catalytic performance. Besides, data-driven methodologies integrating machine learning provide quantitative insights into the spatial organization of metal centers in SACs, thus transcending the isolated atom limit of conventional image analyses. This work advances the understanding and application of SACs, contributing to the development of innovative catalytic materials that address environmental challenges and support sustainable industrial processes.

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