The artificial intelligence-catalyst pipeline: accelerating catalyst innovation from laboratory to industry

Abstract

The integration of high-throughput experimental technologies with artificial intelligence is transforming catalyst research and development. This study explores the synergistic convergence of artificial intelligence and high-throughput experimentation in chemical catalysis, highlighting both current and emerging experimental techniques. It examines how AI-driven methodologies enhance data analysis, automate complex decision-making processes, and optimize catalyst design for industrial applications. The future of research laboratories is envisioned as autonomous, self-driven environments that streamline and accelerate the transition from conceptualization to practical implementation. Key challenges, including data quality, model interpretability, and the scalability of industrial applications, are critically analyzed. Future research should focus on addressing these challenges through strategic methodologies, establishing a systematic framework to fully harness the potential of artificial intelligence and high-throughput experimentation. These advancements will enhance research efficiency and drive innovation in catalysis.

Graphical abstract

Keywords

high-throughput experimentation / artificial intelligence / chemical catalysis / self-driving labs / industrial application

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