Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research. While this process may seem opaque to researchers outside the field of machine learning, examining active learning in games provides an accessible way to showcase the process and its virtues. Here, we examine active learning through the lens of the game Wordle to both explain the active learning process and describe the types of research questions that arise when using active learning for materials research.

Renewable energy applications largely rely on transition metal catalysts. Similar to organocatalysts, p-block elements exhibit transition-metal-like catalytic performances. Si Zhou and co-workers review the latest advances in p-block elements as catalysts for energy conversion to deeply understand the concept of metal-free catalysis and establish the design principles for p-block catalysts.

While the theory of imperfections in solids is firmly established, procedures for first-principles calculations of defect quantities continue to evolve. A plethora of ad hoc correction schemes is being replaced by sophisticated self-consistent procedures that will enable more quantitative predictions of the formation energies of defect species and their spectroscopic signatures.