Tomoyuki Tamura, Masayuki Karasuyama. Prediction of formation energies of large-scale disordered systems via active-learning based executions of ab initio local-energy calculations: a case study on a Fe random grain boundary model with millions of atoms. Physical Review Materials. 2020. 4. 113602-1-113602-13
Hidetoshi Miyazaki, Tomoyuki Tamura, Masashi Mikami, Kosuke Watanabe, Ide Naoki, Osman Murat Ozkendir, Yoichi Nishino. Machine-Learning-based Prediction of Lattice Thermal Conductivity for Half-Heusler Compounds using Atomic Information. arXiv.org. 2020
Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama. Multi-objective Bayesian Optimization using Pareto-frontier Entropy. Proceedings of The 37th International Conference on Machine Learning (ICML 2020). 2020
Masayuki Karasuyama, Hiroki Kasugai, Tomoyuki Tamura, Kazuki Shitara. Computational design of stable and highly ion-conductive materials using multi-objective bayesian optimization: Case studies on diffusion of oxygen and lithium. Computational Materials Science. 2020. 184. 109927
Tomoyuki Tamura, Masayuki Karasuyama. Active-learning-based efficient prediction of ab initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms. arXiv.org. 2019
