| Deep-neural-network solution of the electronic Schrödinger equation J Hermann, Z Schätzle, F Noé Nature Chemistry 12 (10), 891-897, 2020 | 530 | 2020 |
| Electronic excited states in deep variational Monte Carlo MT Entwistle, Z Schätzle, PA Erdman, J Hermann, F Noé Nature Communications 14 (1), 274, 2023 | 37 | 2023 |
| Convergence to the fixed-node limit in deep variational Monte Carlo Z Schätzle, J Hermann, F Noé The Journal of Chemical Physics 154 (12), 2021 | 23 | 2021 |
| DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions Z Schätzle, PB Szabó, M Mezera, J Hermann, F Noé The Journal of Chemical Physics 159 (9), 2023 | 15 | 2023 |
| Deep neural network solution of the electronic schrödinger equation (2019) J Hermann, Z Schätzle, F Noé arXiv preprint arXiv:1909.08423, 0 | 6 | |
| An improved penalty-based excited-state variational Monte Carlo approach with deep-learning ansatzes PB Szabó, Z Schätzle, MT Entwistle, F Noé arXiv preprint arXiv:2405.17089, 2024 | 1 | 2024 |
| An improved penalty-based excited-state variational Monte Carlo approach with deep-learning ansatzes P Bernát Szabó, Z Schätzle, MT Entwistle, F Noé arXiv e-prints, arXiv: 2405.17089, 2024 | | 2024 |
| Machine learning many-electron wave functions via backflow transformations D Luo, BK Clark, D Pfau, JS Spencer, AGG Matthews, WMC Foulkes, ... Phys. Rev. Lett 122, 226401, 2019 | | 2019 |
