| Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents A Ishikawa, K Sodeyama, Y Igarashi, T Nakayama, Y Tateyama, M Okada Physical Chemistry Chemical Physics 21 (48), 26399-26405, 2019 | 44 | 2019 |
| Liquid electrolyte informatics using an exhaustive search with linear regression K Sodeyama, Y Igarashi, T Nakayama, Y Tateyama, M Okada Physical Chemistry Chemical Physics 20 (35), 22585-22591, 2018 | 44 | 2018 |
| Material search for Li-ion battery electrolytes through an exhaustive search with a Gaussian process T Nakayama, Y Igarashi, K Sodeyama, M Okada Chemical Physics Letters 731, 136622, 2019 | 16 | 2019 |
| Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference K Obinata, T Nakayama, A Ishikawa, K Sodeyama, K Nagata, Y Igarashi, ... Science and Technology of Advanced Materials: Methods 2 (1), 355-364, 2022 | 3 | 2022 |
| Comparison of Bayes estimation and variational Bayes estimation in mixed normal distribution model T Nakayama, N Fujii, K Nagata, M Okada IEICE Technical Report; IEICE Tech. Rep. 118 (284), 287-292, 2018 | | 2018 |
| Material search for Li-ion battery electrolytes by exhaustive search with Gaussian Process modeling T Nakayama, Y Igarashi, K Sodeyama, M Okada IEICE Technical Report; IEICE Tech. Rep. 117 (293), 63-68, 2017 | | 2017 |
