Multiscale Simulations for Energy Materials
The Science Partner Journal Energy Material Advances presents a special issue on Multiscale Simulations for Energy Materials.
Theory-guided materials design and data-driven materials discovery have enabled a paradigm shift in materials development. Various theoretical methods are playing increasingly important roles in energy materials research, from functional materials design to energy device design and management. To meet the increasing need for in-depth theoretical understandings and designs, herein we propose a special issue to collect research articles and reviews on the research frontier of multiscale simulations for energy materials, including batteries, fuel cells, solar cells, thermoelectric materials, etc. This special issue aims to bring together the latest progress on the multiscale simulations (including density functional theory, molecular dynamics, Monte-Carlo simulations, phase-field simulations, finite element analysis, pack- and systems-level simulations, and machine learning, etc.) of the energy materials, towards theory-guided, data-driven design and discovery of safe and high-energy-density next-generation energy systems.
Prof. Venkat Viswanathan, Carnegie Mellon University, USA
Venkat Viswanathan is an associate professor in the Department of Mechanical Engineering at Carnegie Mellon University. his research focus is on identifying the scientific principles governing material design, inorganic, organic, and biomaterials, for novel energy conversion and storage routes. The material design is carried out through a suite of computational methods being developed in his group, and validated by experiments. In addition to material design, his group is involved in several cross-cutting areas such as battery controls, electric vehicle security, and GPU accelerated computing.
He received the MIT TR Innovators Under 35 in 2020, Office of Naval Research Young Investigator Award in 2019, Sloan Research Fellowship in Chemistry in 2018, the National Science Foundation CAREER award in 2016, the American Chemical Society PRF Young Investigator Award in 2014, and the Electrochemical Society Daniel Cubicciotti Award in 2010.
Prof. Zijian Hong, Zhejiang University, China
Zijian Hong is currently a faculty member at the School of Materials Science and Engineering, Zhejiang University. He obtained his Ph.D. degree from The Pennsylvania State University (advisor: Prof. Long-Qing Chen) in 2017. Then, he worked as a postdoc research associate at the Department of Mechanical Engineering, Carnegie Mellon University with Prof. Venkat Viswanathan for 3 years. His research mainly focuses on computational materials science, including phase-field simulations, DFT, and machine learning, for specific applications in the design of polar topological phases and metal-based battery systems. He has published more than 40 scientific articles on leading journals, including Nature (3), Nature Materials (3), ACS Energy Letters (4), etc., with ~2000 citations.
Prof. Siqi Shi, Shanghai University, China
Siqi Shi is a professor at the Materials Genome Institute, Shanghai University. He obtained his B.S. and M.S. from Jiangxi Normal University in 1998 and in 2001, respectively. He finished his Ph.D. from Institute of Physics, Chinese Academy of Sciences in 2004. During this period, centered on the electrolyte, electrode materials and relevant interfaces for lithium-ion batteries, he carried out the first-principles calculation and design on the ionic transport physics, cooperative electron/ion transport control problem. After that, he joined the National Institute of Advanced Industrial Science and Technology of Japan and Brown University of USA as a senior research associate until joining Shanghai University as a professor in early 2013. He received the National Natural Science Fund for Excellent Young Scholars in 2016. His current research interests focus on the fundamentals and multiscale calculation of electrochemical energy storage materials and materials design and performance optimization using machine learning.
February 28, 2023
Please select "Special Issue: Multiscale Simulations for Energy Materials" as the section/category during the submission process. Please also indicate in your cover letter that your submission is intended for inclusion in the special issue.