Electrochemical generation of oxygen via the oxygen evolution reaction (OER) is a key enabling step for many air-breathing electrochemical energy storage devices.

Cryo‐electron microscopy (cryo‐EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo‐EM has been drastically improved to generate high‐resolution three‐dimensional maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo‐EM model building approach is template‐based homology modeling. Manual de novo modeling is very time‐consuming when no template model is found in the database. In recent years, de novo cryo‐EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top‐performing methods in macromolecular structure modeling. DL‐based de novo cryo‐EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL‐based de novo cryo‐EM modeling methods. Their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo‐EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence for de novo molecular structure modeling and future directions in this emerging field. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning

The sluggish kinetics of the oxygen evolution reaction (OER) is one of the major sources of the overpotentials in many air-breathing electrochemical energy devices such as electrolyzers and rechargeable metal-air batteries. To increase the OER kinetics and reduce the overpotentials, it is critical to establish the linkage between the catalyst structure and the OER activity and mechanism. In this contribution, we present our effort in establishing this structure-activity connection, using iridates as model electrocatalysts and strain as a tuning knob for understanding the structure-activity connection. We have grown a series of perovskite iridium oxides with different biaxial strains using Molecular Beam Epitaxy (MBE), which we will use as a model system to elucidate how the OER kinetics can be affected by strain. To further connect strain to the physical properties of the iridates, we subject these MBE-grown oxides to ambient pressure X-ray photoelectron spectroscopy, from which we can determine how strain can affect the oxygen adsorption. We use this information to reveal insights into how strain can serve as a material knob to facilitate the OER and the underlying structure-activity relationship in iridates.