Special Issue on Machine Learning and Artificial Intelligence for Energy Materials

With the development of advanced methods in the area of machine learning (ML), artificial intelligence (AI) is rapidly revolutionising many fields and is starting to change the landscapes of Physics and Chemistry. Over the past decade, AI-assisted designs of novel materials have been gradually reshaping how researchers explore new chemistries for energy conversion and storage. In addition, innovative data-driven techniques have led to unprecedented perspectives on the physics and chemistries of the reactions/processes involved. With this background in mind, this themed issue will present a collection of the most recent advances in machine learning and artificial intelligence for energy materials, providing a broad overview of the theoretical advancements and applications of related methods in the areas of energy storage and conversion. The editors welcome original research articles for consideration, as well as reviews.

Topics Covered:

• Theoretical advancements in machine learning and related algorithms for energy materials
• Applications of machine learning and relevant methods in batteries, fuel cells, electrolysis and other applications
• Development of databases for verifying benchmarking novel algorithms
• AI-guided experimental workflows for energy storage and conversion
• Data-driven approaches to the design of novel materials/experiments

Important Deadlines:

• Submission deadline: 16 April 2021

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select “Machine Learning and AI” on submission.

Guest Editors:

• Prof. Francesco Ciucci, The Hong Kong University of Science and Technology, China. Email: francesco.ciucci@ust.hktitle
• Prof. Ziheng Lu, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China. Email: zh.lu1@siat.ac.cn
• Dr. Chi Chen, University of California San Diego, United States. Email: chc273@eng.ucsd.edu