Modulating metal oxides: The more difficult step in fuel cells and water electrolysis is the oxygen evolution reaction. The search for earth-abundant materials to replace noble metals for this reaction often turns to oxides of three-dimensional metals such as iron. Zhang et al. show that the applied voltages needed to drive this reaction are reduced for iron-cobalt oxides by the addition of tungsten. The addition of tungsten favorably modulates the electronic structure of the oxyhydroxide. A key development is to keep the metals well mixed and avoid the formation of separate phases. Science , this issue p. 333

The development of high-performance catalysts for the oxygen-evolution reaction (OER) is paramount for cost-effective conversion of renewable electricity to fuels and chemicals. Here we report the significant enhancement of the OER activity of electrodeposited NiO_x films resulting from the combined effects of using cerium as a dopant and gold as a metal support. This NiCeO_x–Au catalyst delivers high OER activity in alkaline media, and is among the most active OER electrocatalysts yet reported. On the basis of experimental observations and theoretical modelling, we ascribe the activity to a combination of electronic, geometric and support effects, where highly active under-coordinated sites at the oxide support interface are modified by the local chemical binding environment and by doping the host Ni oxide with Ce. The NiCeO_x–Au catalyst is further demonstrated in a device context by pairing it with a nickel–molybdenum hydrogen evolution catalyst in a water electrolyser, which delivers 50 mA consistently at 1.5 V over 24 h of continuous operation.

Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.