The Wonders of Digital Catalysis
Bridging Chemistry and Machine Learning for Homogeneous Catalyst Design
A.V. Kalikadien (TU Delft - ChemE/Inorganic Systems Engineering)
Evgeny A. Pidko – Promotor (TU Delft - ChemE/Inorganic Systems Engineering)
B. Dam – Promotor (TU Delft - ChemE/Materials for Energy Conversion and Storage)
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Abstract
Catalysis lies at the heart of modern society: from producing fuels and fertilizers to manufacturing pharmaceuticals and materials, it enables the chemical transformations that sustain our daily lives. Among the different forms of catalysis, homogeneous catalysis, where well-define molecular complexes drive the production of molecular products, plays a central role in both fundamental research and industrial applications. Yet, the discovery and optimization of catalysts remain resource-intensive, relying heavily on serendipity. The design of transition-metal based homogeneous catalysts remains a central challenge in modern chemistry. While recent advances in artificial intelligence have demonstrated transformative potential across domains such as natural language processing and image generation, their application to molecular design and catalysis has proven more limited. This dissertation explores the integration of high-throughput experimentation, computational chemistry, automation, and machine learning for in silico methodologies aimed at rational design of transition-metal based catalysts. Across eight Chapters, key challenges are addressed in the generation of descriptors, digital representations for machine learning, conformational and configurational flexiblity of ligands and practical examples of machine learning modeling in data-driven catalysis......