Abstract:
I will present our recent works on ab initio modelling and design of materials for next generation metal ion batteries, specifically active electrode materials for post-lithium and organic batteries. I will present key results of comparative studies of intercalation of ions differing by ion size (e.g. Li vs Na vs K) and valence (e.g. Li vs Mg vs Al) in different phases (including amorphous) of several prospective active electrode materials including carbon, Si, Sn, TiO2 and vanadium oxides. I will show how ab initio modelling can help develop efficient strategies to improve voltages or enable electrochemical activity for post-lithium storage. I will highlight our recent advances in computational design of organic electrodes, including MOF-like materials, vdW crystals, and polymers. I will highlight conceptual and methodology issues one has to deal with when modelling battery materials such as the use of oxidation states for rationalization of the mechanism, treatment of vdW interactions or deficiencies of functionals or construction of amorphous phases.
Biography:
Sergei Manzhos is Associate Professor at the Centre Énergy Matériaux Télécommunications (EMT) of the Institut National de la Recherche Scientifique (INRS), Canada. He holds a Ph.D. in chemistry from Queen’s University (2005) and M.Sc. in radio physics from Kharkiv National University (1999). In 2004-2008 he was postdoctoral fellow and NSERC postdoctoral fellow at the Université de Montréal in the group of Prof. Tucker Carrington. He was Project Assistant Professor at the University of Tokyo (Department of Chemical System Engineering and Research Centre for Advanced Science and Technology) in 2008-2012, working in groups of Prof. Koichi Yamashina and Prof. Hiroshi Segawa. In 2012-2019, he was Assistant Professor (group leader) at the Department of Mechanical Engineering, National University of Singapore before joining INRS in 2019.
Prof. Manzhos’s research interests include computational modeling and design of materials for energy conversion and storage technologies as well as method development for computational spectroscopy and large-scale ab initio methods. He uses and develops machine learning based methods for these applications.
Seminar Series by the NYU-ECNU Center for Computational Chemistry at NYU Shanghai