Abstract
This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field of heterogeneous catalysis, presenting a broad spectrum of contemporary methodologies and innovations. We methodically segmented the text into three core areas: catalyst characterization, data-driven exploitation, and data-driven discovery. In the catalyst characterization part, we outline current and prospective techniques used for HTE and how AI-driven strategies can streamline or automate their analysis. The data-driven exploitation part is divided into themes, strategies, and techniques that offer flexibility for either modular application or creation of customized solutions. In the data-driven exploration part we present applications that enable exploration of areas outside the experimentally tested chemical space, incorporating a section on computational methods for identifying new prospects. The review concludes by addressing the current limitations within the field and suggesting possible avenues for future research.
J. Benavides-Hernández, F. Dumeignil, From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design, ACS Catalysis, (2024) 11749-11779. DOI: 10.1021/acscatal.3c06293#Abstract.
