| NetKet 3: Machine learning toolbox for many-body quantum systems F Vicentini, D Hofmann, A Szabó, D Wu, C Roth, C Giuliani, G Pescia, ... SciPost Physics Codebases, 007, 2022 | 74 | 2022 |
| Group convolutional neural networks improve quantum state accuracy C Roth, AH MacDonald arXiv preprint arXiv:2104.05085, 2021 | 36 | 2021 |
| Iterative retraining of quantum spin models using recurrent neural networks C Roth arXiv preprint arXiv:2003.06228, 2020 | 33 | 2020 |
| Kernel rnn learning (kernl) C Roth, I Kanitscheider, I Fiete International Conference on Learning Representations, 2018 | 23 | 2018 |
| High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks C Roth, A Szabó, AH MacDonald Physical Review B 108 (5), 054410, 2023 | 21 | 2023 |
| Spin Frustration and a `Half Fire, Half Ice' Critical Point from Nonuniform -Factors WG Yin, CR Roth, AM Tsvelik arXiv preprint arXiv:1510.00030, 2015 | 4 | 2015 |
| Spin frustration and an exotic critical point in ferromagnets from nonuniform opposite factors W Yin, CR Roth, AM Tsvelik Physical Review B 109 (5), 054427, 2024 | 1 | 2024 |
| Codebase release 3.4 for NetKet F Vicentini, D Hofmann, A Szabó, D Wu, C Roth, C Giuliani, G Pescia, ... SciPost Physics Codebases, 007, 2022 | 1 | 2022 |
| Investigating frustrated magnetism with symmetry-aware neural networks CR Roth | | 2023 |
| Learning the Ground State Wavefunction of Periodic Systems Using Recurrent Neural Networks C Roth, A MacDonald Bulletin of the American Physical Society 65, 2020 | | 2020 |
