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Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems. It leverages the extraordinary data-fitting capacity of modern deep neural networks (DNNs) while preserving high interpretability and robustness by embedding widely applicable physical laws such as the law of mass action and the Arrhenius law. In this paper, we further developed Bayesian CRNN to not only reconstruct but also quantify the uncertainty of chemical kinetic models from data. Two methods, the Markov chain Monte Carlo algorithm and variational inference, were used to perform the Bayesian CRNN, with the latter mainly adopted for its speed. We demonstrated the capability of Bayesian CRNN in the kinetic uncertainty quantification of different types of chemical systems and discussed the importance of embedding physical laws in data-driven modeling. Finally, we discussed the adaptation of Bayesian CRNN for incomplete measurements and model mixing for global uncertainty quantification. 

Abstract Proton conduction underlies many important electrochemical technologies. A family of new proton electrolytes is reported: acid‐in‐clay electrolyte (AiCE) prepared by integrating fast proton carriers in a natural phyllosilicate clay network, which can be made into thin‐film (tens of micrometers) fluid‐impervious membranes. The chosen example systems (sepiolite–phosphoric acid) rank top among the solid proton conductors in terms of proton conductivities (15 mS cm⁻¹at 25 °C, 0.023 mS cm⁻¹at −82 °C), electrochemical stability window (3.35 V), and reduced chemical reactivity. A proton battery is assembled using AiCE as the solid electrolyte membrane. Benefitting from the wider electrochemical stability window, reduced corrosivity, and excellent ionic selectivity of AiCE, the two main problems (gassing and cyclability) of proton batteries are successfully solved. This work draws attention to the element cross‐over problem in proton batteries and the generic “acid‐in‐clay” solid electrolyte approach with superfast proton transport, outstanding selectivity, and improved stability for room‐ to cryogenic‐temperature protonic applications.