Takashi Miyake

Shanghai Institute for Science of Science — China

Materials Science · Physical Sciences

44h-index6.8kcitations238works1.02yr avg
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Research Topics

Magnetic Properties of Alloys(48), Rare-earth and actinide compounds(36), Machine Learning in Materials Science(31), Magnetic and transport properties of perovskites and related materials(30), Physics of Superconductivity and Magnetism(26)

Publications237 total

Covariance linkage assimilation method for unobserved data exploration
Physical Review Materials·2025
Liquid structure of SmFe12-based alloys
Computational Materials Science·2025· 1 cited
Designing single and degenerate flat bands in the kagome lattice with long-range hopping
Physical review. B./Physical review. B·2025· 4 cited
Performance optimization of perovskite solar cells with an automated spin coating system and artificial intelligence technologies
EES solar.·2025· 2 citedOpen Access
Materials Secure Computation with Secret Sharing: A Bayesian Optimization Scheme and Its Performance
Journal of the Physical Society of Japan·2024· 2 citedOpen Access
(Invited) Materials and Processes Developments for the Commercialization of Perovskite Solar Cells
ECS Meeting Abstracts·2024
Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing
Journal of the Society of Materials Science Japan·2024Open Access
Optimizing the composition of (Ce,La)(Co,Fe)<sub>5</sub>for permanent magnet applications using density functional theory
Journal of Physics Condensed Matter·2024
Designing single and degenerate flat bands in the Kagome lattice with long-range hopping
arXiv (Cornell University)·2024Open Access
Towards understanding structure–property relations in materials with interpretable deep learning
npj Computational Materials·2023· 34 citedOpen Access
Deep learning reveals key aspects to help interpret the structure-property relationships of materials
2023Open Access
Materials informatics with machine learning
Elsevier eBooks·2023
Evidence-based data mining method to reveal similarities between materials based on physical mechanisms
Journal of Applied Physics·2023· 4 citedOpen Access
Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content
Frontiers in Materials·2023· 28 citedOpen Access
Pareto Front Analysis and Multi-objective Bayesian Optimization for \((R,Z)\)(Fe,Co,Ti)<sub>12</sub>(<i>R</i>= Y, Nd, Sm;<i>Z</i>= Zr, Dy)
Journal of the Physical Society of Japan·2022· 2 citedOpen Access
Explainable active learning in investigating structure–stability of SmFe12-α-βXαYβ structures X, Y {Mo, Zn, Co, Cu, Ti, Al, Ga}
MRS Bulletin·2022· 12 citedOpen Access
Pareto front analysis and multi-objective Bayesian optimization for (R, Z)(Fe,Co,Ti)12 (R = Y, Nd, Sm; Z = Zr, Dy)
arXiv (Cornell University)·2022Open Access
First-principles study on the stability of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>(</mml:mo><mml:mi>R</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mi>Zr</mml:mi><mml:mo>)</mml:mo><mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>Fe</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mi>Co</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mi>Ti</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>12</mml:mn></mml:msub></mml:math>against 2-17 and unary phases<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="normal">Y</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mi>Nd</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mi>Sm</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math>
Physical Review Materials·2022· 7 citedOpen Access
Function Decomposition Tree with Causality-First Perspective and Systematic Description of Problems in Materials Informatics
arXiv (Cornell University)·2022· 2 citedOpen Access
Peculiar behavior of V on the Curie temperature and anisotropy field of SmFe12-xVx compounds
Acta Materialia·2022· 29 citedOpen Access
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Frequent Co-authors

F. Aryasetiawan(9), Shoji Ishibashi(8), Hiori Kino(8), Silke Biermann(7), Susumu Saito(6), Motoaki Hirayama(6), Antoine Georges(5), Shuichi Murakami(5), Kiyoyuki Terakura(5), Leonid V. Pourovskii(4), Masatoshi Imada(4), Yosuke Harashima(4), Markus Aichhorn(3), Ryo Okugawa(3), Michele Casula(3), Koji Tsuda(3), Taichi Kosugi(3), Ryotaro Arita(3), Hideo Aoki(3), V. Vildosola(2)