Search Results for author: Seiji Takeda

Found 2 papers, 1 papers with code

Generative Toolkit for Scientific Discovery

1 code implementation • 8 Jul 2022 • Matteo Manica, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Jannis Born, Dean Clarke, Yves Gaetan Nana Teukam, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Giorgio Giannone, 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.

Drug Discovery Materials Screening

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Molecular Inverse-Design Platform for Material Industries

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.

Graph Generation Molecular Graph Generation

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