Water-splitting electrolysis, using a renewable power source, has been widely considered as a promising energy conservation and storage technology that is environmentally friendly. In order to lower the required energy barrier and to improve the energy-conversion efficiency of hydrogen evolution and oxygen evolution on the electrodes, highly efficient and durable electrocatalysts are essential. To date, various preparation methods and theoretical models have been developed to accelerate the catalyst design and to further understand the associated electrocatalytic mechanism. In this issue of MRS Bulletin, all aspects of non-noble metal-based electrocatalysts for water splitting involving standard methodology, surface electronic structure engineering, morphology design, interface effects, pH operation range, activity descriptors, and operational stability are discussed. These discussions indicate the importance of materials innovations for the realization of highly efficient and durable electrocatalysts for large-scale cost-effective water splitting.

Applying machine learning (ML) methods to accelerate the search for new materials with improved properties has gained increasing attention in recent years. Using nonadaptive ML approaches that do not have an iterative feedback loop can perform poorly in extrapolations at previously unexplored search space, especially when trained on small data sets. We performed numerical simulations on two data sets that exhibit distinct composition–property relationships and explored the relative efficacies of adaptive ML strategies in identifying the optimal material composition with the highest. Adaptive ML methods show promise for extrapolation and find compositions with properties better than those in the training data, but the rate of discovery is dictated by the nuances of the composition–property landscape. The outcome of this work has key implications in developing strategies that employ ML methods for navigating a vast search space of combinatorial possibilities.

Recent trends in the area of catalytic applications of metal–organic framework (MOF)-derived nanocarbons are covered. These highly porous nanostructures, convenient for the green chemistry processes, are generally formed by the direct carbonization of a variety of MOF, mainly MOF-5, ZIF-8, ZIF-67, UiO-66-NH2, MIL-101-NH2 at 700–1000 °C in argon or nitrogen flow. Differences between conventional porous carbons and MOF-derived carbons are in pore volumes, surface area, and presence of ad-atoms. The morphology of the MOF-derived nanocarbons can be adjustable with uniform dopant distribution. Resulting nanocarbons are widely applied in heterogeneous catalysis, photocatalysis and are very promising as electrocatalysts, having excellent performance in oxygen evolution reaction, oxygen reduction reaction, and hydrogen evolution reaction. Catalytic applications for environmental purposes are also discussed. Good catalytic performance is related with highly dispersed heteroatoms, density of catalytic active sites, controllable porosity, and high surface area. Opportunities for further research are indicated, in particular, the creation of low pH-stable electrocatalysts and novel strategies for the preparation of 1÷3D single-atom catalysts.

In this study, the quasi-static and dynamic mechanical behaviors and the energy absorption capacity of closed-cell aluminum foams with uniform and graded densities were experimentally studied. The effects of density, strain rate, and graded density on the mechanical performances of aluminum foams were quantitatively evaluated. It was shown that the density had a significant effect on the quasi-static and dynamic compressive stress of aluminum foams. Moreover, impact compression experiment results revealed that aluminum foam was sensitive to the strain rate. As the strain rate increased, the plateau stress and energy absorption capacity increased distinctly and the rate of deformation increased correspondingly. Finally, the investigation of aluminum foams with uniform and graded densities to study their deformation and failure mechanisms, mechanical characteristics, and energy absorption capacities showed that the GD 0.48-IV specimen exhibited superior impact resistance. The present work can provide a valuable reference for the optimum design of aluminum foam against impact loading.

This study investigates the effect of C on the deformation mechanisms in Fe–C alloys by molecular dynamics simulations. In uniaxial tensile simulations, the face-centered-cubic (fcc) structures of Fe–C alloys undergo the following deformation processes: (i) fcc→body-centered-cubic (bcc) martensitic transformation, (ii) deformation of bcc phase, and (iii) bcc→hcp martensitic transformation, which are significantly influenced by the C concentration. For the low C concentrations (0–0.8 wt%) fcc phase, the fcc→bcc phase transformation accords a two-stage shear transformation mechanism based on the Bain model, the deformation mechanism of the bcc phase is the first migration of twinning structures and then elastic deformation, and the bcc→hcp phase transformation follows Burgers relations resulting from the shear of the bcc close-packed layers. However, for the fcc phase with high C concentrations (1.0–2.0 wt%), the fcc→bcc phase transformation follows a localized Bain transformation mechanism impeded by the C atoms, the bcc phase only experiences elastic deformation, and the bcc→hcp phase transformation also conforms to Burgers relations but become localized due to the addition of more C atoms. Because of the different phase transformation mechanisms between the high C and low C supercells, the dislocation generation mechanism is also different.