Designing Electrochemical Energy Storage Materials Through Combining High-throughput Calculations and Machine-learning
Jianjun Liu
Integrated Computational Materials Scientific Research Center, Shanghai institute of Ceramics, Chinese
Academy of Sciences, 1295 Dingxi Road, Shanghai 200050, China
EXTENDED ABSTRACT: Developing high-efficiency computational electrochemical method is an important tool of exploring new electrode materials and optimizing electrochemical storage performance. Based on electrochemical reaction enthalpy gradient change induced by charge transfer, we have developed computational method of quantitatively characterizing electrochemical activity. Furthermore, we constructed high-throughput intelligent computational platform for electrochemical energy storage material design. In this talk, I introduce these methods being applied in lithium battery materials and hydrogen evolution catalysts for solving new material design and performance optimization. We found that surface acidity and interfacial charge transfer ability are important screening criteria for reducing overpotential between charge and discharge voltage difference. Through high-throughput calculations, we predicted spinel MnCo2O4 should be an effective cathode material, which is experimentally confirmed to have 0.3 V overpotential (commonly 1.0 eV over) and 400 cycles. The local structure electronegativity is also used to screen Li-S cathode materials for suppressing LiPSs shutting effect from cathode to anode. By using this high-throughput intelligent computational platform, we further revealed electronegativity and electron affinity of local structure are screening criteria of electrocatalyst in hydrogen evolution. We computationally predict three-atom doped two-dimension catalyst MoS23Co-V which can effectively reduce to 170mV overpotential, which is far lower than the previous reports. Therefore, the present studies indicate that combining high-throughput calculations and machine-learning techniques cannot only provide deep insight on understanding materials’ physical and chemical properties, but also establish effective strategy for new material design and performance optimization of classical materials
Professor of Shanghai Institute of Ceramics, CAS, who is also recipients of CAS Outstanding Talent (2012) and Shanghai Outstanding Academic Leaders (2020). He got his Ph D. degree from the Institute of Theoretical Chemistry of Jilin University in 2002. After that, he became a short-term visiting scholar in MAX-PLANCK-INSTITUT. In 2003, he worked as a postdoctoral fellow at Scientific Computational Center of Emory University in USA. In 2005, he became an assistant scientist in Southern Illinois University in USA. In 2012, he came back to china and worked in Shanghai Institute of Ceramics. His main research field is computational electrochemical chemistry and material design. He has published about 100 academic papers in the famous journals such as Nature Commun, Science Advances, Chem, J Am Chem Soc, Angew Chem Int Ed, Adv Mater, Energy Environ Sci. He is also the authors of four book chapters of which he was designated as book editors. In the past several years, he undertake several key projects such as Key R&D Projects (MGE), Key and General projects of NSFC, Outstanding Talent and Key projects from CAS and Shanghai government