Designing electrochemical Storage
Materials Through Local Structure Properties
Jianjun Liu*
Integrated Computational Materials Scientific Research Center, Shanghai institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai 200050, China
ABSTRACT: The relationship for material composition-structure effect on property is a fundamental base for designing new materials and optimizing material performance based on high-throughput computational methods and machine-learning models, as well as an important part of materials genomic engineering. In this talk, taking electrochemical storage material as an example, the comprehensive methods including first-principles calculations, machine-learning methods, and electrochemical experimental characterizations were used to study the relationship between local structure property and electroactivity, and reveal local structure electronegativity/electron affinity as screening and design rule of electrocatalyst and electrodes. Putting it in hydrogen evolution electrocatalyst in fuel cell, we computationally predict three-atom doped two-dimension catalyst MoS23Co-V which can effectively reduce overpotential. Through machine learning and high-throughput methods, we further predict more TMD catalysts which are consistent with experimental results. The local structure property is also applied in design charge reaction catalyst of Li-O2 battery. Electrochemical experiments show that computational predicting spinel MnCo2O4 has high catalytic activity with 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. Not limited for these, the local structure properties are used to design organic electrodes and Li-rich cathodes with experimental comparison. Therefore, these studies indicate that combining calculation and experimental techniques cannot 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.
Keywords: Electrochemical storage material; local structure; high throughput calculations; machine learning
Jianjun Liu, Professor of Shanghai Institute of Ceramics, CAS, who is also recipients of CAS Outstanding Talent and Shanghai Outstanding Academic Leaders. 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, 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.