Application and optimization of deep learning algorithms for mechanistic prediction of physicochemical reactions

Accurate prediction of physical-chemical reaction mechanisms is a critical foundation for understanding and controlling chemical reaction processes, as well as advancing the development of new materials and drug design. Traditional methods based on quantum chemistry and molecular simulation face high computational costs, low efficiency, and limited applicability when dealing with complex reaction systems. As a key branch of artificial intelligence, deep learning has emerged as an important tool in reaction mechanism prediction due to its powerful feature extraction and pattern recognition capabilities. This paper provides a systematic review of the research progress and current applications of deep learning algorithms in the prediction of physical-chemical reaction mechanisms, with a focus on analyzing the advantages and limitations of deep learning models in reaction pathway prediction, reaction kinetics simulation, and reaction energy calculation. Addressing issues such as data scarcity, insufficient interpretability, and low computational efficiency in existing models, this paper proposes a systematic optimization approach encompassing data-driven optimization, model performance enhancement, and interpretability improvement. Combining the cross-disciplinary trends of artificial intelligence and physical chemistry, the paper also explores the broad application prospects of deep learning technology in automated reaction design, virtual screening, and the construction of closed-loop systems for experimental verification. This paper aims to provide new insights and technical support for future research on physical chemistry reaction mechanisms and to promote the continuous development of intelligent chemical research.