Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
arXiv:2606.23757v1 Announce Type: cross 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,
The increasing availability of complex-systems data and advancements in AI/ML techniques for scientific discovery enable more sophisticated approaches to chemical and biological modeling.
This development allows for more accurate and efficient discovery of complex chemical reaction networks, which is crucial for advancing fields like materials science, drug discovery, and synthetic biology.
The ability to extract interpretable governing equations from noisy chemical data will accelerate R&D cycles by improving understanding of underlying chemical processes and optimizing experimental design.
- · Pharmaceuticals
- · Chemical engineering
- · Materials science
- · AI/ML in scientific discovery
- · Traditional high-throughput screening methods
- · Trial-and-error chemical R&D
Accelerated discovery of new molecules and materials with improved properties.
Reduced costs and time-to-market for products dependent on complex chemical synthesis.
Enhanced therapeutic capabilities and industrial processes through optimized biochemical pathways.
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Read at arXiv cs.AI