Ai / Machine Learning–Enabled Intelligent Frameworks for NextGen Catalyst Discovery and Performance Optimization

Abstract

Authors: Dr. Rishabh K. Menon, Dr. Priyanka S. Hegde

ABSTRACT: Catalyst discovery has historically relied on slow, resource-intensive experimental procedures and complex theoretical modeling. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled transformative approaches that accelerate the identification, optimization, and deployment of catalysts for chemical, energy, and environmental applications. This paper presents a comprehensive overview of how AI/ML models enhance catalyst screening, predict structure–property relationships, and guide autonomous laboratory systems. The paper highlights major computational strategies such as deep learning, generative modeling, active learning, and high-throughput virtual screening. Applications in heterogeneous, homogeneous, electrocatalysis, photocatalysis, and enzyme catalysis are also discussed. Finally, the challenges, limitations, future opportunities, and research directions for integrating AI-driven catalyst discovery frameworks are presented, demonstrating their potential to significantly reduce development timelines and reduce costs in the chemical sciences.

KEYWORDS: Artificial intelligence, Machine learning, Catalyst discovery, High-throughput screening, Generative models, Materials informatics, Reaction optimization, Autonomous laboratories.