Yicheng Zhao

Personal Information

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Professor   Supervisor of Doctorate Candidates  

Honors and Titles:Excellent Doctoral Thesis of Peking University
Humboldt Fellow
National Talents Plan

Personal Profile


Prof. Dr. Yicheng Zhao | Principal Investigator Yicheng Zhao is a junior professor in the School of Electronic Science and Engineering at the University of Electronic Science and Technology of China (UESTC). He was honoured with Humboldt Fellow in 2018, Hundred Talents of UESTC and National Talents Plan in 2022. From 2018 to 2022, he worked in the University of Erlangen Nuremberg (FAU) and Helmholtz Institute (HiERN) in Germany (with Prof. Christoph J. Brabec);  From 2013 to 2018, He studied in The School of Physics at the Peking University (with Academician Dapeng Yu and Prof. Qing Zhao). He had one-year experience in the Edward H. Sargent Research Group at University of Toronto, Canada. In recent years, his work has been published as the first/corresponding author in Nature Energy, Nature Communications, Advanced Materials etc. with over 5900 citations.



Educational Experience

  • Peking University
  • 凝聚态物理
  • Doctor of Science
  • With Certificate of Graduation for Doctorate Study

  • Xinjiang University
  • 物理学
  • 理学学士学位
  • With Certificate of Graduation for Undergraduate Study

Work ExperienceMore>>

2022.5 Now
  • University of Electronic Science and Technology of China
  • State Key Laboratory of Electronic Thin Films and Integrated Devices
  • Professor
2020.11 2022.5
  • Helmholtz Institute Erlangen
  • Research Scientist
2018.9 2020.11
  • Erlangen University
  • WW6
  • Humboldt Fellow

Social Affiliations

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    • Research FocusMore>>

    • "High-throughput experiment + Machine learning" intelligent experiment platform
    • Multi-element complex semiconductors
    • Novel metal-halide perovskite photovoltaic devices

    Research Group

    The Zhao Group @UESTC is mainly engaged in the development of "High-throughput Experiment + Machine Learning" intelligent experimental platform and the applications of complex semiconductor materials and electronic devices. High-throughput experiments can realize the preparation and characterization of complex materials and devices, while machine learning provides the capability of real-time analysis, optimization and interpretation of big data produced by high-throughput experiments. The core tools of high-throughput experiments include automated multi-channel pipetting systems, drop/spin coating methods, fluorescence detection, UV-vis-IR absorption spectroscopy, photoelectric detection, expectation-maximization-based spectroscopy analysis, Gaussian-Process Regression algorithm, SHAP explainer etc.. Our devices include novel metal-halide perovskite semiconductors, organic semiconductors and colloidal quantum dots for photovoltaic, sensing and detection applications.
    Recommended Ph.D.Supervisor

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