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Combinatorial Approach to Materials Discovery,Machine Learning, and Topological Superconductor

Date: 2018-10-18
Time: 14:00
Venue: M234
Speaker: ProfessorIchiro Takeuchi

Department of Materials Science and Engineering, University of Maryland


报告分两个部分: (Part I)Combinatorial Approach to Materials Discovery; (Part II)Machine Learning Modeling of Superconductivity and Observation of Perfect Andreev Reflection due to Klein Paradox in a Topological Superconductor

The high-throughput concept is now widely implemented in a variety of fields in materials science. We have developed combinatorial thin film synthesis and characterization techniques in order to perform rapid survey of previously unexplored materials phase space in search of new inorganic functional materials. I will discuss our recent work on data driven strategies to discovery and integration of the combinatorial experimental approach with theory as well as machine learning.

In one, we have used machine learning to develop models of the superconducting critical temperature in an effort to come up with systematic predictions for possible new superconductors based on a comprehensive experimental database containing over 14000 different superconductors. The random forest based models can correctly capture the essential features of different classes of superconductors. By combining machine learning models and computed electronic structure information from ICSD, we have come up with a list of possible new superconductors.

In another effort, we have carried out point contact spectroscopy on proximity induced superconductivity in a topological Kondo insulator SmB6. When the thickness of the SmB6 layer in the SmB6/YB6 bilayers is within 20-30 nm, we observe perfect Andreev reflection as a consequence of Klein paradox. The observation can be described by the Dirac-BTK model, where spin-momentum locking on both sides of the interface gives rise to exact doubling of the conductance within the gap of the superconductor and the barrier Z is effectively zero.


Ichiro Takeuchi is a Professor of Materials Science and Engineering and Affiliate Professor of Physics at the University of Maryland. Takeuchi obtained his Bachelor of Science in physics from California Institute of Technology and his PhD in physics from the University of Maryland. Takeuchi is the Graduate Program Director in the Department of Materials Science and Engineering at Maryland. Takeuchi was a postdoctoral researcher at Lawrence Berkeley National Laboratory where he helped pioneer the combinatorial materials discovery methodology. He previously also spent a number of years as a technical staff at NEC Corporation where he worked on thin film materials for superconducting electronics. Takeuchi is a fellow of the American Physical Society. He won the University of Maryland Invention of the Year Award in 2010 for invention of elastocaloric cooling. Takeuchi was awarded the University of Maryland Distinguished Scholar Teacher and the Engineering College Outstanding Faculty Research Award in 2018. 

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