no code implementations • 24 Apr 2020 • Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu, Victoria A. Piunova, Dmitry Zubarev, Daniel P. Sanders, Jed W. Pitera, Makoto Kogoh, Takumi Hongo, Yenwei Cheng, Wolf Bocanett, Hideaki Nakashika, Akihiro Fujita, Yuta Tsuchiya, Katsuhiko Hino, Kentaro Yano, Shuichi Hirose, Hiroki Toda, Yasumitsu Orii, Daiju Nakano
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance.
1 code implementation • 8 Jul 2022 • Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
no code implementations • 28 Sep 2023 • Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini, Lisa Hamada, Hajime Shinohara, Daiju Nakano, Seiji Takeda
Property prediction plays an important role in material discovery.
1 code implementation • 20 Oct 2023 • Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil, Seiji Takeda, Hiroshi Kajino, Renato Cerqueira
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.