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Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Yuma Iwasaki
Ryohto Sawada
Shinichi Yorozu
ResearchOpen Access30 Oct 2019
npj Computational Materials

Volume: 5, P: 1-6
Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training

Ryohto Sawada
Yuma Iwasaki
Masahiko Ishida
ResearchOpen Access13 May 2020
Scientific Reports

Volume: 10, P: 1-6
Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

Existing neural network potentials are generally designed for narrow target materials. Here the authors develop a neural network potential which is able to handle any combination of 45 elements and show its applicability in multiple domains.

So Takamoto
Chikashi Shinagawa
Takeshi Ibuka
ResearchOpen Access30 May 2022
Nature Communications

Volume: 13, P: 1-11
Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit

Finding materials with large magnetization is highly desirable for technological applications. Here, a machine learning autonomous search and automated combinatorial synthesis reveal that multi-element alloys with Ir and Pt impurities have a magnetization exceeding the Slater-Pauling limit of Fe₇₅Co₂₅.

Yuma Iwasaki
Ryohto Sawada
Masahiko Ishida
ResearchOpen Access19 Mar 2021
Communications Materials

Volume: 2, P: 1-7
High-throughput investigation of stability and Li diffusion of doped solid electrolytes via neural network potential without configurational knowledge

Ryohto Sawada
Kosuke Nakago
So Takamoto
ResearchOpen Access21 May 2024
Scientific Reports

Volume: 14, P: 1-8
Machine-learning guided discovery of a new thermoelectric material

Yuma Iwasaki
Ichiro Takeuchi
Shinichi Yorozu
ResearchOpen Access26 Feb 2019
Scientific Reports

Volume: 9, P: 1-7

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Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training

Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit

High-throughput investigation of stability and Li diffusion of doped solid electrolytes via neural network potential without configurational knowledge

Machine-learning guided discovery of a new thermoelectric material

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