We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By closing this message, you are consenting to our use of cookies.

Materials Informatics

Science and Technology of Advanced Materials

Back to Special Collections

This special collection focuses on the new discipline of Materials Informatics, which is a fusion of Materials Science and Information Science. Novel data-driven technologies bring enormous possibilities to a variety of applications by covering the relations between processing, morphology (structure) and properties of materials.

STAM supports the development of Materials Informatics and its community not only by providing this special collection, but also by hosting a dedicated web portal: here.

Article TitleAuthor
Automatic steel labeling on certain microstructural constituents with image processing and machine learning toolsDmitry S. Bulgarevich, Susumu Tsukamoto, Tadashi Kasuya, Masahiko Demura & Makoto Watanabe
Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materialsYukari Katsura, Masaya Kumagai, Takushi Kodani, Mitsunori Kaneshige, Yuki Ando, Sakiko Gunji, Yoji Imai, Hideyasu Ouchi, Kazuki Tobita, Kaoru Kimura & Koji Tsuda
Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysisTarojiro Matsumura, Naoka Nagamura, Shotaro Akaho, Kenji Nagata & Yasunobu Ando
Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materialsYukari Katsura, Masaya Kumagai, Takushi Kodani, Mitsunori Kaneshige, Yuki Ando, Sakiko Gunji, Yoji Imai, Hideyasu Ouchi, Kazuki Tobita, Kaoru Kimura & Koji Tsuda
Automatic steel labeling on certain microstructural constituents with image processing and machine learning toolsDmitry S. Bulgarevich, Susumu Tsukamoto, Tadashi Kasuya, Masahiko Demura & Makoto Watanabe
Data-driven exploration of new pressure-induced superconductivity in PbBi2Te4Ryo Matsumoto, Zhufeng Hou, Masanori Nagao, Shintaro Adachi, Hiroshi Hara, Hiromi Tanaka, Kazuki Nakamura, Ryo Murakami, Sayaka Yamamoto, Hiroyuki Takeya, Tetsuo Irifune, Kiyoyuki Terakura & Yoshihiko Takano
Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocityTakeshi Onishi, Takuya Kadohira & Ikumu Watanabe
Focus on materials genome and informaticsKiyoyuki Terakura & Ichiro Takeuchi
Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field modelShin-ichi Ito, Hiromichi Nagao, Tadashi Kasuya & Junya Inoue
Petascale supercomputing to accelerate the design of high-temperature alloysDongwon Shin, Sangkeun Lee, Amit Shyam & J. Allen Haynes
Machine learning reveals orbital interaction in materialsTien Lam Pham, Hiori Kino, Kiyoyuki Terakura, Takashi Miyake, Koji Tsuda, Ichigaku Takigawa & Hieu Chi Dam
MDTS: Automatic Complex Materials Design using Monte Carlo Tree SearchThaer M. Dieb, Shenghong Ju, Kazuki Yoshizoe, Zhufeng Hou, Junichiro Shiomi & Koji Tsuda
Quantitative description on structure-property relationships of Li-ion battery materials for high-throughput computationsYouwei Wang, Wenqing Zhang, Lidong Chen, Siqi Shi & Jianjun Liu
ChemTS: an efficient python library for de novo molecular generationXiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama & Koji Tsuda
A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property dataRandy Jalem, Masanobu Nakayama, Yusuke Noda, Tam Le, Ichiro Takeuchi, Yoshitaka Tateyama & Hisatsugu Yamazaki