| Predicting materials properties with little data using shotgun transfer learning H Yamada, C Liu, S Wu, Y Koyama, S Ju, J Shiomi, J Morikawa, ... ACS central science 5 (10), 1717-1730, 2019 | 298 | 2019 |
| Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning S Ju, R Yoshida, C Liu, S Wu, K Hongo, T Tadano, J Shiomi Physical Review Materials 5 (5), 053801, 2021 | 49 | 2021 |
| Machine learning to predict quasicrystals from chemical compositions C Liu, E Fujita, Y Katsura, Y Inada, A Ishikawa, R Tamura, K Kimura, ... Advanced Materials 33 (36), 2102507, 2021 | 41 | 2021 |
| iQSPR in xenonpy: a bayesian molecular design algorithm S Wu, G Lambard, C Liu, H Yamada, R Yoshida Molecular informatics 39 (1-2), 1900107, 2020 | 30 | 2020 |
| Crystal structure prediction with machine learning-based element substitution M Kusaba, C Liu, R Yoshida Computational Materials Science 211, 111496, 2022 | 24 | 2022 |
| Recreation of the periodic table with an unsupervised machine learning algorithm M Kusaba, C Liu, Y Koyama, K Terakura, R Yoshida Scientific reports 11 (1), 4780, 2021 | 13 | 2021 |
| Full-potential KKR calculations for point defect energies in Fe-based dilute alloys, based on the generalized-gradient approximation C Liu, M Asato, N Fujima, T Hoshino Materials Transactions 54 (9), 1667-1672, 2013 | 10 | 2013 |
| Full-Potential KKR Calculations for Lattice Distortion Effect of Point Defect in bcc-Fe Dilute Alloys, Based on the Generalized-Gradient Approximation M Asato, C Liu, K Kawakami, N Fujima, T Hoshino Materials transactions 55 (8), 1248-1256, 2014 | 9 | 2014 |
| Ab-Initio Calculations for Solvus Temperatures of Pd-Rich PdRu Alloys: Real-Space Cluster Expansion and Cluster Variation Method C Liu, M Asato, N Fujima, T Hoshino, Y Chen, T Mohri Materials Transactions 59 (3), 338-347, 2018 | 7 | 2018 |
| Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach P Torres, S Wu, S Ju, C Liu, T Tadano, R Yoshida, J Shiomi Journal of Physics: Condensed Matter 34 (13), 135702, 2022 | 5 | 2022 |
| Interaction Energies Among Rh Impurities in Pd and Solvus Temperatures of Pd-Rich PdRh Alloys C Liu, M Asato, N Fujima, T Hoshino, Y Chen, T Mohri Materials transactions 59 (6), 883-889, 2018 | 5 | 2018 |
| Full-Potential KKR calculations for Lattice Distortion around Impurities in Al-based dilute alloys, based on the Generalized-Gradient Approximation C Liu, M Asato, N Fujima, T Hoshino Physics Procedia 75, 1088-1095, 2015 | 5 | 2015 |
| Functional output regression for machine learning in materials science M Iwayama, S Wu, C Liu, R Yoshida Journal of Chemical Information and Modeling 62 (20), 4837-4851, 2022 | 4 | 2022 |
| Real Space Cluster Expansion for Total Energies of Pd-Rich PdX (X= Rh, Ru) Alloys, Based on Full-Potential KKR Calculations: An Approach from a Dilute Limit C Liu, M Asato, N Fujima, T Hoshino, Y Chen, T Mohri Materials transactions 59 (11), 1669-1676, 2018 | 4 | 2018 |
| Accuracy of Real Space Cluster Expansion for Total Energies of Pd-rich PdX (X= Rh, Ru) Alloys, based on Full-Potential KKR Calculations for Perfect and Impurity Systems M Asato, C Liu, N Fujima, T Hoshino, Y Chen, T Mohri MATEC Web of Conferences 264, 03002, 2019 | 3 | 2019 |
| Shotgun crystal structure prediction using machine-learned formation energies C Liu, H Tamaki, T Yokoyama, K Wakasugi, S Yotsuhashi, M Kusaba, ... arXiv preprint arXiv:2305.02158, 2023 | 1 | 2023 |
| Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks Q Zhang, C Liu, S Wu, R Yoshida arXiv preprint arXiv:2204.01847, 2022 | 1 | 2022 |
| Machine Learning to Predict Quasicrystals from Chemical Compositions (Adv. Mater. 36/2021) C Liu, E Fujita, Y Katsura, Y Inada, A Ishikawa, R Tamura, K Kimura, ... Advanced Materials 33 (36), 2170284, 2021 | 1 | 2021 |
| マテリアルズインフォマティクス概説 吉田亮 統計数理: 特集 「マテリアルズインフォマティクスの最前線」 69, 5-33, 2021 | 1 | 2021 |
| Full-Potential KKR Calculations for Interaction Energies in Al-Rich AlX (X= H∼ Sn) Alloys: I. Fundamental Features and Thermal Electronic Contribution due to Fermi-Dirac … M Asato, C Liu, N Fujima, T Hoshino, T Mohri Materials transactions 61 (1), 94-103, 2020 | 1 | 2020 |
