Search Results for author: Yoshihiko Takano

Found 4 papers, 2 papers with code

Semi-automatic staging area for high-quality structured data extraction from scientific literature

1 code implementation19 Sep 2023 Luca Foppiano, Tomoya Mato, Kensei Terashima, Pedro Ortiz Suarez, Taku Tou, Chikako Sakai, Wei-Sheng Wang, Toshiyuki Amagasa, Yoshihiko Takano, Masashi Ishii

For manual operations, the interface (SuperCon2 interface) is developed to increase efficiency during manual correction by providing a smart interface and an enhanced PDF document viewer.

Anomaly Detection

Automatic extraction of materials and properties from superconductors scientific literature

2 code implementations26 Oct 2022 Luca Foppiano, Pedro Baptista de Castro, Pedro Ortiz Suarez, Kensei Terashima, Yoshihiko Takano, Masashi Ishii

Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records from 37700 papers.

 Ranked #1 on NER on SuperMat

NER

High-$T_c$ superconducting hydrides formed by LaH$_{24}$ and YH$_{24}$ cage structures as basic blocks

no code implementations11 Mar 2021 Peng Song, Zhufeng Hou, Pedro Baptista de Castro, Kousuke Nakano, Kenta Hongo, Yoshihiko Takano, Ryo Maezono

Based on recent studies regarding high-temperature (high-$T_c$) La-Y ternary hydrides (e. g., $P{\bar{1}}$-La$_2$YH$_{12}$, $Pm{\bar{3}}m$-LaYH$_{12}$, and $Pm{\bar{3}}m$-(La, Y)H$_{10}$ with a maximum $T_c \sim 253$ K), we examined the phase and structural stabilities of the (LaH$_6$)(YH$_6$)$_y$ series as high-$T_c$ ternary hydride compositions using a genetic algorithm and $\it ab$ $\it initio$ calculations.

Superconductivity Computational Physics

Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB$_{2}$ Near Hydrogen Liquefaction Temperature

no code implementations12 May 2020 Pedro Baptista de Castro, Kensei Terashima, Takafumi D Yamamoto, Zhufeng Hou, Suguru Iwasaki, Ryo Matsumoto, Shintaro Adachi, Yoshito Saito, Peng Song, Hiroyuki Takeya, Yoshihiko Takano

Magnetic refrigeration exploits the magnetocaloric effect which is the entropy change upon application and removal of magnetic fields in materials, providing an alternate path for refrigeration other than the conventional gas cycles.

BIG-bench Machine Learning

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