结合物理约束与化学先验，自动发现反应网络并优化产率。
Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery
- 用物理约束的MCMC采样反应路径，避免错误拟合。
- 在苯乙烯环氧化中提升产率12.5%，优于传统方法。
- 适合需要可解释性反应机理的催化研究者。
从稀疏、噪声大的化学时序数据中提取可解释的控制方程仍具挑战，因离散反应拓扑与连续动力学参数紧密耦合。我们提出PC-MCMC-CIGP，一种可复现的灰箱工作流，融合了尖峰-泥浆拓扑采样、硬守恒与热力学筛选，以及化学先验的高斯过程（CIGP）残差模型，用于参数校准与实验设计。方法创新不在于单一的新MCMC或GP族，而在于将这些组件整合进一个具有物理约束的流程，并支持显式的不确定性感知采集策略。在H2 + Br2基准测试中，该约束采样器成功区分了基元自由基路径与误导性的表观拟合。在苯乙烯环氧化任务中，CIGP优化循环使最终产率较报告的GP-BO基线提升12.5%。一项新的10种子采集研究显示：期望改进（EI）、广义加权不确定性（GWU）、物理约束期望改进（PC-EI）、不确定性采样、差异性探测与随机搜索各有优劣：PC-EI显著减少低产率建议，而EI类准则带来最优最终产率。
原文摘要 · Abstract (English)
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.
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