Materials Frontiers 2024 Issue 15 (TOTAL ISSUE 83)
June 27, 2024 15:00 ~ 16:30 Yiucheng Lecture Hall (500), Xu Zuyao Building

Title: Materials informatics for heat transfer and beyond

Speaker: Prof. Junichiro Shiomi, Assistant Dean of School of Engineering, the University of Tokyo

Date/Time: 2024.6.27 15:00-16:30

Location: Yiucheng Lecture Hall (500), Xu Zuyao Building

Inviter: Assoc.Prof. Kehang Cui



Dr. Junichiro Shiomi is Professor in Institute of Engineering Innovation and Department of Mechanical Engineering, School of Engineering, the University of Tokyo. He is also currently Assistant Dean of School of Engineering, the University of Tokyo. He received B.E. (1999) from Tohoku University, and Ph. D. (2004) from Royal Institute of Technology (KTH), Sweden. Leading the Thermal Energy Engineering Lab, he has been pursuing research to advance thermal management, waste heat recovery, and energy harvesting technologies based on nano-to-macro innovation in materials, structures, and systems. Dr. Shiomi has been leading many research projects including Grant-in-Aid for Scientific Research (S) (JSPS), Core Research for Evolutional Science and Technology (JST-CREST), JST-ASPIRE, and New Energy and Industrial Technology Development Organization (NEDO) projects. He is Fellow of Japan Society of Mechanical Engineers and Member of Science Council of Japan. He serves as an associate editor of Nanoscale and Microscale Thermophysical Engineering. He is a recipient of the Zeldovich Medal from the Committee on Space Research, the Commendation for Science and Technology by the Minister of Educational, Culture, Sports, Science and Technology, the Academic award of Heat Transfer Society of Japan, the Academic Award of Thermoelectric Society of Japan, the JSPS Prize, and the Nukiyama Memorial Award.


Materials informatics (MI) is to develop or study materials with an aid of informatics or machine learning. A typical approach is to train a black box model that relates basic descriptors (structure, composition, etc) and figure-or-merit (target properties) and predict or design a material with the best (or good) performance. At Thermal Energy Engineering Lab (TEEL) at University of Tokyo, together with the collaborators, we have been working on MI for heat transfer since 2015. One of the initial works was to design binary multilayered nanostructure to minimize or maximize thermal conductance by coupling thermal transport calculation and Bayesian optimization, which showed excellent efficiency. Later, the search space has been greatly expanded by utilizing quantum annealing. We have applied the methodology to computationally design and experimentally realize aperiodic superlattice that optimally impedes coherent thermal transport and multilayer metamaterial with wavelength-selective thermal radiation.

   More recently, we have extended the machine-learning approach to that for polymers, aiming to functionalize them in terms of the thermal and dielectric properties. When molding or compounding polymers, the final properties are quite sensitive to the process parameters, therefore, the above approach of serially connecting optimal design and experimental realization of materials is not sufficient. To this end, we have been developing a semi-automated MI system, where experimental fabrication and measurement are included in the optimization loop (experiment in the loop). This allows us to efficiently enhance the polymer composite properties.

   I will introduce these series of works on MI for complex materials at TEEL and discuss associated practical and conceptual issues.