Machine learning approaches for transition state prediction

Abstract

Searching for a transition state (TS) is crucial in understanding chemical reaction mechanisms and kinetics. While traditional computational methods, including single-ended and double-ended approaches, have provided valuable insights, they face significant computational cost and scalability limitations. This review comprehensively examines conventional computational approaches and the rapidly emerging machine learning (ML) methods for TS searching, highlighting the significant acceleration in ML method development since 2020. We first analyze traditional computational methods, discussing their theoretical foundations and practical limitations. We then systematically review available TS datasets that enable ML applications. The review explores the evolution of ML approaches from traditional methods like random forest and kernel ridge regression to advanced architectures such as graph neural networks, tensor field networks, and generative models. We examine current challenges, including data scarcity, computational constraints, and validation standards, while highlighting promising future directions. This comprehensive analysis provides insights into the field’s current state and outlines potential pathways for advancing TS searching methodologies.