Yukinori Koyama

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National Institute for Materials Science

Japan

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材料インフォマティクスのスモールデータ問題に対する階層的機械学習モデリング

Open Date: 2018-04-01

Close Date: 2021-03-31

Grant: Close

BC系新規リチウムインサーション材料の物性評価

Open Date: 2008-04-01

Close Date: 2010-03-01

Grant: Close

ビスマス酸化物系高速酸化物イオン導電体を用いたイオニックデバイス用電極

Open Date: 2007-04-01

Close Date: 2009-03-01

Grant: Close

量子化学計算に基づいた次世代リチウムイオン電池用正極材料の開発

Open Date: 2002-04-01

Close Date: 2005-03-01

Articles (9)

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2025

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2024

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2023

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2023

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2022

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2022

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2021

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Tokyo Institute of Technology

JAPAN

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Yukinori Koyama

Research Interests

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Contact this professor

Recent Grants

Articles (9)

Periodic table-based compositional descriptors for accelerating electrochemical material discovery: Li-ion conductors and oxygen evolution electrocatalysts

Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences

A high voltage aqueous proton battery using an optimized operation of a MoO3 positive electrode

Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach

Narrow-Band Emitting Phosphor Na2Cs2Sr(B9O15)2:Eu2+ Discovered from Local Structure Similarity with Sulfate Phosphor

Narrow-band phosphor K2ZnP2O7:Eu2+ discovered using local structure similarity

Dissimilarity measure of local structure in inorganic crystals using Wasserstein distance to search for novel phosphors

Recreation of the periodic table with an unsupervised machine learning algorithm

Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides

Collaborators (5)

Isao Tanaka

Liu Chang

Hidekazu Ikeno

穫草場

Atsunori Ikezawa

Make your next move.

Yukinori Koyama

Research Interests

Explore related searches

Contact this professor

Recent Grants

Articles (9)

Periodic table-based compositional descriptors for accelerating electrochemical material discovery: Li-ion conductors and oxygen evolution electrocatalysts

Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences

A high voltage aqueous proton battery using an optimized operation of a MoO3 positive electrode

Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach

Narrow-Band Emitting Phosphor Na2Cs2Sr(B9O15)2:Eu2+ Discovered from Local Structure Similarity with Sulfate Phosphor

Narrow-band phosphor K2ZnP2O7:Eu2+ discovered using local structure similarity

Dissimilarity measure of local structure in inorganic crystals using Wasserstein distance to search for novel phosphors

Recreation of the periodic table with an unsupervised machine learning algorithm

Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides

Collaborators (5)

Isao Tanaka

Liu Chang

Hidekazu Ikeno

穫 草場

Atsunori Ikezawa

Make your next move.

穫草場