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They're using predictive models to decide which experiment to do next. It's as AI as anything else. > The OER is a thermodynamically uphill reaction involving
by __MatrixMan__ 3y ago
They're using predictive models to decide which experiment to do next. It's as AI as anything else.
> The OER is a thermodynamically uphill reaction involving four consecutive oxidation steps and O–O bond formation, which requires an applied voltage of no less than 1.23 V to operate. The OER overpotential, which is defined as the extra voltage above 1.23 V required for catalysis to occur, characterizes the voltage efficiency of the electrochemical device. Therefore, we chose the measured overpotential as the primary target of our ML model in searching for the optimal OER catalyst10,11,12. We first created 29,902 unique compositions and simulated atomic structures of resulting high-entropy hydroxides (Fig. 2a) from classical MD simulations (Supplementary Figs. 11 and 12). The obtained structural features, such as averaged metal–metal and metal–oxygen distances (Supplementary Figs. 13 and 14 and Supplementary Table 2), are passed to previously established bimetallic hydroxide models13,14 (Fig. 2b) to determine the OER activity of each multimetallic hydroxide by DFT calculation. Three DFT-predicted OER activity descriptors—including the Gibbs free energy change of hydroxyl adsorption ΔGOH* (ref. 15) and differences between the Gibbs free energy change for oxygen adsorption and hydroxyl adsorption ΔGO−OH (ref. 16), the amount of charge transferred for hydroxyl adsorption on the activate site Δq (ref. 17)—and the paired composition information are used for NN training. As Fig. 2c shows, the NN model can accurately reproduce these DFT results. With the NN model, we can now rapidly predict the OER activity of high-entropy hydroxides obtained from any given composition of selected Martian ores (Supplementary Fig. 15), and these theoretical values are then connected with experimentally measured overpotentials. The ML model achieves remarkable accuracy in predicting true overpotentials (Fig. 2d).