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Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different traditional and emerging approaches to demonstrate their inherent connection and highlight the evolving paradigms of materials design., Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.

Amorphous oxides are one of the most active catalysts for the oxygen evolution reaction (OER). However, very little is known about the structure of the amorphous oxide catalyst during OER, especial...

Controlling the structure of catalysts at the atomic level provides an opportunity to establish detailed understanding of the catalytic form-to-function and realize new, non-equilibrium catalytic structures. Here, advanced thin-film deposition is used to control the atomic structure of La2/3Sr1/3MnO3, a well-known catalyst for the oxygen reduction reaction. The surface and sub-surface is customized, whereas the overall composition and d-electron configuration of the oxide is kept constant. Although the addition of SrMnO3 benefits the oxygen reduction reaction via electronic structure and conductivity improvements, SrMnO3 can react with ambient air to reduce the surface site availability. Placing SrMnO3 in the sub-surface underneath a LaMnO3 overlayer allows the catalyst to maintain the surface site availability while benefiting from improved electronic effects. The results show the promise of advanced thin-film deposition for realizing atomically precise catalysts, in which the surface and sub-surface structure and stoichiometry are tailored for functionality, over controlling only bulk compositions., Controlling structures at the atomic level provides an opportunity to design and understand catalysts. Here the authors use thin-film deposition to fabricate perovskite heterostructures in a non-equilibrium manner to assess the effects on electrocatalytic activity for oxygen reduction.

Understanding how physicochemical properties of materials affect the oxygen evolution reaction (OER) has enormous scientific and technological implications for the OER electrocatalyst design. We pr...