Fig. 3: Autonomous closed-loop workflow of mechanism discernment and electrokinetic analysis.

a The workflow of Stage I for DL-based discernment of an EC mechanism via Bayesian optimization. A system is considered to bear an EC mechanism when the maximized DL-generated propensity of an EC mechanism exceeds 50%. b The trajectory of a 15-step campaign of Bayesian optimization when RX = n-BuBr. [RX] ∈ [0.008, 1000] mM and ν_min ∈ [0.01, 0.2] V/s. c The initial response surface of i_pa/i_pc as a function of ν and [n-BuBr] (ν ∈ [0.01, 2] V/s and [n-BuBr] ∈ [0.008, 1000] mM), created via a Gaussian process, after completion of Stage I. d The workflow of Stage II for adaptive search of parameter combinations suitable for electrokinetic analysis. e Desired combinations of [n-BuBr] and ν that satisfy i_pa/i_pc ∈ [0.65, 0.75] are found at 2 and 9 [n-BuBr] values in Stage I and II, respectively, resulting in a linear regression of log₁₀(k_obs) versus log₁₀[n-BuBr] for the quantification of k₀ value. The vertical error bars represent the standard deviations of log₁₀(k_obs) among all the valid k_obs values at each [n-BuBr] value. f The on-the-fly updated response surface of i_pa/i_pc after completion of Stage II. The colors of the surfaces in (c) and (f) represent the local model uncertainty from the Gaussian process (the standard deviation of the modeled i_pa/i_pc). The fluctuations in the surfaces in (c) and (f) could be ascribed to the fact that a small number of data points are fitted to a large parameter space. Data distributions underlying the error bars in (d) are presented in Source Data file.