Layering the charge: Layered metal oxides such as lithium cobalt oxide have attracted great attention for rechargeable batteries. In lithium cells, only the octahedral structure forms, but in sodium cells, trigonal prismatic structures are also possible. However, there is a lack of understanding about how to predict and control the formation of each structure. Zhao et al. used the simple properties of ions, namely their charge and their radius appropriately weighted by stoichiometry, to determine whether sodium in the interlayers between the transition metal or other ion-oxide layers remain octahedral rather than switching over to trigonal prismatic coordination. Science , this issue p. 708

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

Layered Na-based oxides with the general composition NaxTMO2 (TM=transition metal) have attracted significant attention for their high compositional diversity that provides tuneable electrochemical performance for positive electrodes in in sodi-um-ion batteries. The various compositions bring forward complex structural chemistry that is decisive for the layered stack-ing structure, Na-ion conductivity and the redox-activity, potentially promising new avenues in functional material proper-ties. In this work, we have explored the maximum Na content in these P2-type layered oxides, and discovered that the high-content Na in the host enhances the structural stability, moreover, it promotes the oxidation of low-valent cations to their high oxidation states (in this case Ni2+). This can be rationalized by the increased hybridization of the O(2p)-TM(3d-eg) states, affecting both the local TM environment as well as the interactions between the NaO2 and TMO2 layers. These properties are highly beneficial for the Na storage capabilities as required for positive electrodes in sodium-ion batteries. It leads to excel-lent Na-ion mobility, a large storage capacity (>100 mAh g−1), yet preventing the detrimental sliding of the TMO2 layers (P2-O2 structural transition), as reflected by the ultra-long cycle life (3000 (dis)charge cycles demonstrated). These findings expand the horizons of high Na-content P2-type materials, providing new insights of the electronic and structural chemistry for advanced cathode materials.