Ryo Tamura

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Coautors

Koji TsudaProfessor, GSFS, The University of TokyoCorreu electrònic verificat a k.u-tokyo.ac.jp
Kei TerayamaYokohama City UniversityCorreu electrònic verificat a yokohama-cu.ac.jp
Shu TanakaAssociate Professor, Department of Applied Physics and Physico-Informatics, Keio UniversityCorreu electrònic verificat a appi.keio.ac.jp
Masato SumitaRIKENCorreu electrònic verificat a riken.jp
Naoki KawashimaInstitute for Solid State Physics, University of TokyoCorreu electrònic verificat a issp.u-tokyo.ac.jp
Genki YoshikawaNational Institute of Materials Science (NIMS)Correu electrònic verificat a nims.go.jp
Kosuke MinamiNational Institute for Materials ScienceCorreu electrònic verificat a nims.go.jp
Shinsuke IshiharaNational Institute for Materials ScienceCorreu electrònic verificat a nims.go.jp
Yoshitaro NoseKyoto UniversityCorreu electrònic verificat a kyoto-u.ac.jp
Masanobu NaitoNational Institute for Materials ScienceCorreu electrònic verificat a nims.go.jp
Guillaume DeffrennesResearch Associate at CNRS (SIMaP laboratory)Correu electrònic verificat a cnrs.fr
Tsuyoshi MiyazakiMANA-PI/Group Leader, National Institute for Materials ScienceCorreu electrònic verificat a nims.go.jp
Koji HukushimaUniversity of TokyoCorreu electrònic verificat a g.ecc.u-tokyo.ac.jp
Junichiro ShiomiThe University of TokyoCorreu electrònic verificat a photon.t.u-tokyo.ac.jp
Toshio Osada物質・材料研究機構　超耐熱材料グループCorreu electrònic verificat a nims.go.jp
Victorino FrancoProfessor of Condensed Matter Physics, Sevilla UniversityCorreu electrònic verificat a us.es
Hidenori HiramatsuTokyo Institute of TechnologyCorreu electrònic verificat a mces.titech.ac.jp
Hosho KatsuraUniversity of TokyoCorreu electrònic verificat a phys.s.u-tokyo.ac.jp
Shoichi MatsudaNational Institute for Material ScienceCorreu electrònic verificat a nims.go.jp
Cedric TasselICMCB and ENSMAC, Bordeaux INPCorreu electrònic verificat a bordeaux-inp.fr

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Ryo Tamura

National Institute for Materials Science

Correu electrònic verificat a nims.go.jp - Pàgina d'inici

Materials informatics Machine learning Frustrated spin systems Magnetocaloric effect


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Designing metamaterials with quantum annealing and factorization machines K Kitai, J Guo, S Ju, S Tanaka, K Tsuda, J Shiomi, R Tamura Physical Review Research 2 (1), 013319, 2020	163	2020
Quantum Spin Glasses, Annealing and Computation S Tanaka, R Tamura, BK Chakrabarti Cambridge University Press, 2017	160	2017
Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies M Sumita, X Yang, S Ishihara, R Tamura, K Tsuda ACS central science 4 (9), 1126-1133, 2018	139	2018
Black-Box Optimization for Automated Discovery K Terayama, M Sumita, R Tamura, K Tsuda Accounts of Chemical Research 54 (6), 1334-1346, 2021	132	2021
Machine learning-driven optimization in powder manufacturing of Ni-Co based superalloy R Tamura, T Osada, K Minagawa, T Kohata, M Hirosawa, K Tsuda, ... Materials & Design 198, 109290, 2021	75	2021
Applicability of scaling behavior and power laws in the analysis of the magnetocaloric effect in second-order phase transition materials C Romero-Muñiz, R Tamura, S Tanaka, V Franco Physical Review B 94 (13), 134401, 2016	65	2016
Bayesian optimization package: PHYSBO Y Motoyama, R Tamura, K Yoshimi, K Terayama, T Ueno, K Tsuda Computer Physics Communications 278, 108405, 2022	60	2022
Optimization of a Heterogeneous Ternary Li₃PO₄–Li₃BO₃–Li₂SO₄ Mixture for Li-Ion Conductivity by Machine Learning K Homma, Y Liu, M Sumita, R Tamura, N Fushimi, J Iwata, K Tsuda, ... The Journal of Physical Chemistry C 124 (24), 12865-12870, 2020	55	2020
Data-driven nanomechanical sensing: specific information extraction from a complex system K Shiba, R Tamura, G Imamura, G Yoshikawa Scientific Reports 7 (1), 3661, 2017	55	2017
Efficient construction method for phase diagrams using uncertainty sampling K Terayama, R Tamura, Y Nose, H Hiramatsu, H Hosono, Y Okuno, ... Physical Review Materials 3 (3), 033802, 2019	54	2019
Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis K Shiba, R Tamura, T Sugiyama, Y Kameyama, K Koda, E Sakon, ... ACS sensors 3 (8), 1592-1600, 2018	47	2018
Phase transition in Potts model with invisible states R Tamura, S Tanaka, N Kawashima Progress of theoretical physics 124 (2), 381-388, 2010	46	2010
First-Order Transition to Incommensurate Phase with Broken Lattice Rotation Symmetry in Frustrated Heisenberg Model R Tamura, N Kawashima J. Phys. Soc. Jpn. 77 (10), 103002, 2008	45	2008
Entanglement spectra of the quantum hard-square model: Holographic minimal models S Tanaka, R Tamura, H Katsura Physical Review A 86 (3), 032326, 2012	40	2012
Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures T Suzuki, R Tamura, T Miyazaki International Journal of Quantum Chemistry 117 (1), 33-39, 2017	38	2017
De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning M Sumita, K Terayama, N Suzuki, S Ishihara, R Tamura, MK Chahal, ... Science advances 8 (10), eabj3906, 2022	36	2022
Pushing property limits in materials discovery via boundless objective-free exploration K Terayama, M Sumita, R Tamura, DT Payne, MK Chahal, S Ishihara, ... Chemical science 11 (23), 5959-5968, 2020	36	2020
Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus H Wang, H Zhang, B Da, D Lu, R Tamura, K Goto, I Watanabe, D Fujita, ... Nano Letters 21 (3), 1538-1545, 2021	30	2021
Method for estimating spin-spin interactions from magnetization curves R Tamura, K Hukushima Physical Review B 95 (6), 064407, 2017	28	2017
A machine learning–based classification approach for phase diagram prediction G Deffrennes, K Terayama, T Abe, R Tamura Materials & Design 215, 110497, 2022	27	2022

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