Emerging Technologies for Materials Design and Characterization in Energy Conversion and Storage

In today's society, many important progresses have been made in the study of energy conversion and storage. However, with the advance of research in depth and breadth, the traditional methods in materials design and characterization are increasingly unable to meet such a rich and diverse materials system. Fortunately, some new and advanced technologies have emerged from the disciplines of computer science, biology and physics, such as deep learning, fingerprinting, cryo-electron microscopy (cryo-EM), etc. The emergence of these technologies has brought new opportunities to the materials discipline, and it is hoped that some bottlenecks currently encountered will be resolved. For example, some pioneering scientists have begun to use deep learning methods to design, screen, and optimize materials in order to greatly improve the electrochemical energy conversion and storage performance of existing materials or guide the preparation of new materials to achieve revolutionary breakthroughs in material properties. A few research teams have started to use cryo-EM to characterize sensitive materials that could not be characterized by common electron microscopy, such as the metal Li in lithium-ion batteries. This Research Topic aims to systematically summarize recent research progresses and highlight research works on the latest technologies for materials design and characterization in energy conversion and storage processes.

This Research Topic is focused on the rising techniques, such as deep learning, cryo-EM, in-situ (synchrotron radiation) et al., and their prospects in materials design and characterization for electrochemical energy conversion and storage processes. This includes but is not limited to lithium-ion batteries and beyond (e.g., sodium, potassium, magnesium, zinc), electrocatalytic reactions (e.g., hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, CO₂ reduction reaction, nitrogen reduction reaction, methanol oxidation reaction, ethanol oxidation reaction), and thermoelectric conversion.

We particularly welcome the submission of Original Research papers, Reviews, and short communications on the following topics:
1) Artificial intelligence, machine learning, and deep learning for materials design in energy conversion and storage processes
2) Cryo-EM and in-situ TEM for materials characterization in energy conversion and storage processes
3) In-situ/operando synchrotron radiation, in-situ/operando neutron techniques, and in-situ/operando morphology/spectrum techniques for materials characterization in energy conversion and storage processes