[2606.23757] Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
[Submitted on 22 Jun 2026]
Title:Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
View a PDF of the paper titled Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery, by Runzhe Liu and 2 other authors
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Abstract:Extracting interpretable governing equations from sparse, noisy chemical time-series data remains difficult because discrete reaction topology and continuous kinetic parameters are tightly coupled. We present PC-MCMC-CIGP, a reproducible gray-box workflow that combines spike-and-slab topology sampling, hard conservation and thermodynamic screening, and a Chemical-Informed Gaussian Process (CIGP) residual model for parameter calibration and experimental design. The methodological contribution is not a new MCMC or GP family in isolation; rather, it is the integration of these components into a physically constrained workflow with explicit uncertainty-aware acquisition choices. On the H2 + Br2 benchmark, the constrained sampler distinguishes elementary radical pathways from deceptive phenomenological fits in our experiments. On styrene epoxidation, the CIGP optimization loop improves final yield by 12.5% over the reported GP-BO baseline. A new 10-seed acquisition study shows that EI, GWU, PC-EI, uncertainty sampling, discrepancy hunting, and random search have different trade-offs: PC-EI substantially reduces low-yield BO suggestions, while EI-style criteria give the strongest final-yield performance.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.23757 [cs.LG]
(or arXiv:2606.23757v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.23757
arXiv-issued DOI via DataCite
Submission history
From: Zihao Wang [view email] [v1] Mon, 22 Jun 2026 08:43:04 UTC (2,254 KB)
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