| Quantum Spin Glasses, Annealing and Computation S Tanaka, R Tamura, BK Chakrabarti Cambridge University Press, 2017 | 153 | 2017 |
| 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 | 149 | 2020 |
| 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 | 135 | 2018 |
| Black-Box Optimization for Automated Discovery K Terayama, M Sumita, R Tamura, K Tsuda Accounts of Chemical Research 54 (6), 1334-1346, 2021 | 122 | 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 | 72 | 2021 |
| Applicability of scaling behavior and power laws in the analysis of the magnetocaloric effect in second-order phase transition materials C Romero-Muņiz, R Tamura, S Tanaka, V Franco Physical Review B 94 (13), 134401, 2016 | 69 | 2016 |
| Bayesian optimization package: PHYSBO Y Motoyama, R Tamura, K Yoshimi, K Terayama, T Ueno, K Tsuda Computer Physics Communications 278, 108405, 2022 | 53 | 2022 |
| 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 | 53 | 2019 |
| 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 | 53 | 2017 |
| 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 | 51 | 2020 |
| 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 | 46 | 2018 |
| Phase transition in Potts model with invisible states R Tamura, S Tanaka, N Kawashima Progress of theoretical physics 124 (2), 381-388, 2010 | 45 | 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 | 44 | 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 | 37 | 2017 |
| 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 |
| 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 | 33 | 2022 |
| Efficient recommendation tool of materials by an executable file based on machine learning K Terayama, K Tsuda, R Tamura Japanese Journal of Applied Physics 58 (9), 098001, 2019 | 28 | 2019 |
| 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 |
| 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 | 27 | 2021 |
Koji TsudaProfessor, GSFS, The University of TokyoVerified email at k.u-tokyo.ac.jp
