KinFormer: Generalizable Dynamical Symbolic Regression for Catalytic Organic Reaction Kinetics
Jindou Chen Jidong Tian Liang Wu ChenXinWei Xiaokang Yang Yaohui Jin Yanyan Xu
Modeling kinetic equations is essential for understanding the mechanisms of chemical reactions, yet a complex and time-consuming task.  Kinetic equation prediction is formulated as a problem of dynamical symbolic regression (DSR) subject to physical chemistry constraints. Deep learning (DL) holds the potential to capture reaction patterns and predict kinetic equations from data of chemical species, effectively avoiding empirical bias and improving efficiency compared with traditional analytical methods. Despite numerous studies focusing on DSR and the introduction of Transformers to predict ordinary differential equations, the corresponding models lack generalization abilities across diverse categories of reactions. In this study, we propose KinFormer, a generalizable kinetic equation prediction model. KinFormer utilizes a conditional Transformer to model DSR under physical constraints and employs Monte Carlo Tree Search to apply the model to new types of reactions. Experimental results on 20 types of organic reactions demonstrate that KinFormer not only outperforms classical baselines, but also  exceeds Transformer baselines in out-of-domain evaluations, thereby proving its generalization ability.
Kinetic Equation Prediction, Dynamical Symbolic Regression, Transform; Conditional Strategy, MCTS
We propose a generalizable Transformer-MCTS framework for chemical reaction kinetic equation prediction