Tokyo Institute of Technology, Tokyo, Japan
Teruaki Hayakawa is a Professor in the Department of Materials Science and Engineering at the School of Materials and Chemical Technology at Tokyo Institute of Technology. He currently serves as Associate Dean of the same school.
He earned his B.S. (1995), M.S. (1997), and Ph.D. (2000) degrees from Yamagata University, conducting research in polymer materials science and engineering based on polycondensation under the guidance of Prof. Mitsuru Ueda. From 1997 to 1998, he was a visiting scholar in the lab of Prof. Christopher K. Ober at Cornell University. In 2000, he joined the National Institute of Advanced Industrial Science and Technology (AIST) as a researcher. He moved to Tokyo Institute of Technology in 2003 as an Assistant Professor, was promoted to Associate Professor in 2009, and became a Full Professor in 2017. His research has focused on the development of directed self-assembly block copolymer materials, high thermal conductive epoxy materials, and wholly aromatic condensation polymers. He chaired the Research Group on Polymers for Microelectronics and Photonics of the Society of Polymer Science, Japan (SPSJ) from 2012 to 2014. He served as an Executive Director of SPSJ from 2018 to 2020. He is currently Vice President of The Photopolymer Science and Technology. He is also an Associate Editor of Materials Today Chemistry. His accolades include the SPSJ Award for Encouragement of Research in Polymer Science 2005, the SPSJ Award for the Outstanding Paper in Polymer Journal 2006 sponsored by ZEON, the SPSJ Hitachi Chemical Award 2010, the SPSJ Wiley Award 2015, and the Best Paper Awards in 2016 and 2020 in the field of photopolymer science and technology.
The integration of liquid crystalline properties into polymer chains, which promotes a regular arrangement, significantly reduces phonon scattering and thereby enhances thermal conductivity. This study aimed to develop novel liquid-crystalline polyimides using a materials informatics approach based on a virtual library. Initially, a polyimide template with six segments was designed with synthesizability in mind. Subsequently, a virtual library of 115,872 polyimides was created by incorporating purchasable units from the ZINC database into the template segments. A machine learning model based on XenonPy, pre-trained using the PolyInfo database, was employed to predict the likelihood of the polyimides existing in a liquid crystalline phase (LC probability). From this virtual library, eight polyimides with a high LC probability were selected for synthesis, as depicted in Figure. One of these polyimides demonstrated a relatively high in-plane thermal conductivity of 0.9 W/(m·K).
Figure 1. Schematic illustration of data-driven design of polyimides.
Acknowledgment: This work was supported by JST, CREST Grant Number JPMJCR19I3, and JST, the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2112.
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