Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery

The paper introduces PC-MCMC-CIGP, a gray-box workflow that integrates spike-and-slab topology sampling with Chemical-Informed Gaussian Processes (CIGP) for enhancing reaction network discovery from sparse chemical data. It demonstrates improved parameter calibration and experimental design, achieving a 12.5% increase in yield on styrene epoxidation compared to a Gaussian Process Bayesian Optimization baseline. This approach is significant for practitioners as it effectively combines MCMC and GP methods under physical constraints, optimizing decision-making in experimental setups while addressing uncertainty in chemical reactions.