Zobrazeno 1 - 10
of 649
pro vyhledávání: '"Takeda Seiji"'
Autor:
Priyadarsini, Indra, Takeda, Seiji, Hamada, Lisa, Brazil, Emilio Vital, Soares, Eduardo, Shinohara, Hajime
Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of transformers, models now learn representations directly from molecular
Externí odkaz:
http://arxiv.org/abs/2410.12348
Autor:
Priyadarsini, Indra, Sharma, Vidushi, Takeda, Seiji, Kishimoto, Akihiro, Hamada, Lisa, Shinohara, Hajime
Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery electrolytes rely on complex interactions between the individual constitu
Externí odkaz:
http://arxiv.org/abs/2406.19792
Autor:
Soares, Eduardo, Kishimoto, Akihiro, Brazil, Emilio Vital, Takeda, Seiji, Kajino, Hiroshi, Cerqueira, Renato
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
Externí odkaz:
http://arxiv.org/abs/2310.13802
Autor:
Kishimoto, Akihiro, Kajino, Hiroshi, Hirose, Masataka, Fuchiwaki, Junta, Priyadarsini, Indra, Hamada, Lisa, Shinohara, Hajime, Nakano, Daiju, Takeda, Seiji
Property prediction plays an important role in material discovery. As an initial step to eventually develop a foundation model for material science, we introduce a new autoencoder called the MHG-GNN, which combines graph neural network (GNN) with Mol
Externí odkaz:
http://arxiv.org/abs/2309.16374
Autor:
Subagyo Agus, Sueoka Kazuhisa, Takeda Seiji, Nakamura Motonori, Ishii Atsushi, Hosoi Hirotaka, Mukasa Koichi
Publikováno v:
Nanoscale Research Letters, Vol 2, Iss 4, Pp 207-212 (2007)
AbstractWe fabricated a pH-sensitive device on a glass substrate based on properties of carbon nanotubes. Nanotubes were immobilized specifically on chemically modified areas on a substrate followed by deposition of metallic source and drain electrod
Externí odkaz:
https://doaj.org/article/f9e9a7abc7f84912959f3ee1907ae8c5
Autor:
Manica, Matteo, Born, Jannis, Cadow, Joris, Christofidellis, Dimitrios, Dave, Ashish, Clarke, Dean, Teukam, Yves Gaetan Nana, Giannone, Giorgio, Hoffman, Samuel C., Buchan, Matthew, Chenthamarakshan, Vijil, Donovan, Timothy, Hsu, Hsiang Han, Zipoli, Federico, Schilter, Oliver, Kishimoto, Akihiro, Hamada, Lisa, Padhi, Inkit, Wehden, Karl, McHugh, Lauren, Khrabrov, Alexy, Das, Payel, Takeda, Seiji, Smith, John R.
Publikováno v:
Nature Partner Journals (npj) Computational Materials 9, 69 (2023)
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hy
Externí odkaz:
http://arxiv.org/abs/2207.03928
Autor:
Giro, Ronaldo, Hsu, Hsianghan, Kishimoto, Akihiro, Hama, Toshiyuki, Neumann, Rodrigo F., Luan, Binquan, Takeda, Seiji, Hamada, Lisa, Steiner, Mathias B.
The generation of molecules with Artificial Intelligence (AI) is poised to revolutionize materials discovery. Potential applications range from development of potent drugs to efficient carbon capture and separation technologies. However, existing com
Externí odkaz:
http://arxiv.org/abs/2206.14634
Autor:
Takeda, Seiji, Hama, Toshiyuki, Hsu, Hsiang-Han, Kishimoto, Akihiro, Kogoh, Makoto, Hongo, Takumi, Fujieda, Kumiko, Nakashika, Hideaki, Zubarev, Dmitry, Sanders, Daniel P., Pitera, Jed W., Fuchiwaki, Junta, Nakano, Daiju
Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is particularly important owing to its broad application domains and boundl
Externí odkaz:
http://arxiv.org/abs/2108.03044
Autor:
Takeda, Seiji, Hama, Toshiyuki, Hsu, Hsiang-Han, Piunova, Victoria A., Zubarev, Dmitry, Sanders, Daniel P., Pitera, Jed W., Kogoh, Makoto, Hongo, Takumi, Cheng, Yenwei, Bocanett, Wolf, Nakashika, Hideaki, Fujita, Akihiro, Tsuchiya, Yuta, Hino, Katsuhiko, Yano, Kentaro, Hirose, Shuichi, Toda, Hiroki, Orii, Yasumitsu, Nakano, Daiju
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial design space of material structures exceeds human experts' capability to e
Externí odkaz:
http://arxiv.org/abs/2004.11521
Autor:
Takeda, Seiji, Hama, Toshiyuki, Hsu, Hsiang-Han, Yamane, Toshiyuki, Masuda, Koji, Piunova, Victoria A., Zubarev, Dmitry, Pitera, Jed, Sanders, Daniel P., Nakano, Daiju
Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) w
Externí odkaz:
http://arxiv.org/abs/2001.09038