no code implementations • 14 Dec 2023 • Shun Muroga, Takashi Honda, Yasuaki Miki, Hideaki Nakajima, Don N. Futaba, Kenji Hata
To meet the demands for more adaptable and expedient approaches to augment both research and manufacturing, we report an autonomous system using real-time in-situ characterization and an autonomous, decision-making processer based on an active learning algorithm.
no code implementations • 27 Nov 2023 • Shun Muroga, Satoshi Yamazaki, Koji Michishio, Hideaki Nakajima, Takahiro Morimoto, Nagayasu Oshima, Kazufumi Kobashi, Toshiya Okazaki
The results show how phase lags (asynchronous changes from stimuli) and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena like the removal of amorphous carbon and graphitization in annealed CNTs.
no code implementations • 29 Mar 2023 • Shun Muroga, Yasuaki Miki, Kenji Hata
We present a multimodal deep learning (MDL) framework for predicting physical properties of a 10-dimensional acrylic polymer composite material by merging physical attributes and chemical data.