Versatile catalyst systems with large current density under industrial conditions are pivotal to give impetus to hydrogen energy from fundamental to practical applications. Herein, a Schottky heterojunction nanosheet array composed of dispersed NiFe hydroxide nanoparticles and ultrathin NiS nanosheets (NiFe LDH/NiS) is proposed to regulate cooperatively mass transport and electronic structure for triggering oxygen evolution reaction (OER) activity at high current. In catalytic systems, the rich porosity of the NiS nanosheet array contributes abundant catalytic sites and good infiltration of the electrolyte for fast mass transfer. Furthermore, theoretical calculations reveal the coupling of NiFe LDH onto the NiS could tune the d‐band center of Ni(Fe) atoms and the binding strength of oxygen intermediates for favorable OER kinetics. Therefore, the NiFe LDH/NiS Schottky heterojunction exhibits a remarkable OER activity, delivering a current density of 1000 mA cm–2 at the ultralow overpotential of 325 mV. Meanwhile, scaled‐up NiFe LDH/NiS electrodes are implemented in an industrial water splitting electrolyzer and exhibit a stable cell voltage of 2.01 V to deliver a constant catalytic current of 8000 mA over 80 h, saving 0.215 kWh of electricity to generate more hydrogen per cubic meter than commercial Raney Ni electrodes.

This paper proposes an approximate dynamic programming (ADP) based approach for the economic dispatch (ED) of microgrid with distributed generations (DGs). The timevariant renewable generation, electricity price and the power demand are considered as stochastic variables in this work. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo (MC) method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intraday optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.

Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the integration of machine learning algorithms and large data resources, the data‐driven methods have opened up a new road for the development of polymer science and engineering. The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new polymers by using machine learning models based on reliable data. With the gradual supplement of currently available databases, the emergence of new databases and the continuous improvement of machine learning algorithms, the research paradigm of polymer informatics will be more efficient and widely used. Based on these points, this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduction for researchers in materials, artificial intelligence, and other fields.

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