| nabladft: Large-scale conformational energy and hamiltonian prediction benchmark and dataset K Khrabrov, I Shenbin, A Ryabov, A Tsypin, A Telepov, A Alekseev, ... Physical Chemistry Chemical Physics 24 (42), 25853-25863, 2022 | 24 | 2022 |
| Neural network interpolation of exchange-correlation functional A Ryabov, I Akhatov, P Zhilyaev Scientific reports 10 (1), 8000, 2020 | 23 | 2020 |
| Process parameter selection for production of stainless steel 316L using efficient multi-objective Bayesian optimization algorithm T Chepiga, P Zhilyaev, A Ryabov, AP Simonov, ON Dubinin, DG Firsov, ... Materials 16 (3), 1050, 2023 | 14 | 2023 |
| Comment on “Pushing the frontiers of density functionals by solving the fractional electron problem” IS Gerasimov, TV Losev, EY Epifanov, I Rudenko, IS Bushmarinov, ... Science 377 (6606), eabq3385, 2022 | 9 | 2022 |
| Application of two-component neural network for exchange-correlation functional interpolation A Ryabov, I Akhatov, P Zhilyaev Scientific Reports 12 (1), 14133, 2022 | 4 | 2022 |
| A Method for Auto-Differentiation of the Voronoi Tessellation S Shumilin, A Ryabov, S Barannikov, E Burnaev, V Vanovskii arXiv preprint arXiv:2312.16192, 2023 | 2 | 2023 |
| On the practical applicability of modern DFT functionals for chemical computations. Case study of DM21 applicability for geometry optimization K Kulaev, A Ryabov, M Medvedev, E Burnaev, V Vanovskiy arXiv preprint arXiv:2501.12149, 2025 | | 2025 |
| Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation S Shumilin, A Ryabov, N Yavich, E Burnaev, V Vanovskiy Forty-first International Conference on Machine Learning, 0 | | |
Petr ZhilyaevSkolkovo Institute of Science and Technology在 skoltech.ru 的电子邮件经过验证
