| SchNetPack: A deep learning toolbox for atomistic systems KT Schütt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller Journal of chemical theory and computation 15 (1), 448-455, 2018 | 465 | 2018 |
| Estimation of thermodynamic observables in lattice field theories with deep generative models KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... Physical Review Letters 126 (3), 032001, 2021 | 130 | 2021 |
| Asymptotically unbiased estimation of physical observables with neural samplers KA Nicoli, S Nakajima, N Strodthoff, W Samek, KR Müller, P Kessel Phys. Rev. E 101 (2), 023304, 2020 | 128 | 2020 |
| Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 98* | 2022 |
| Gradients should stay on path: better estimators of the reverse-and forward KL divergence for normalizing flows L Vaitl, KA Nicoli, S Nakajima, P Kessel Machine Learning: Science and Technology 3 (4), 045006, 2022 | 30 | 2022 |
| Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories KA Nicoli, CJ Anders, T Hartung, K Jansen, P Kessel, S Nakajima Phys. Rev. D 108, 114501 108 (11), 2023 | 28 | 2023 |
| Physics-informed bayesian optimization of variational quantum circuits K Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, S Kühn, KR Müller, ... Advances in Neural Information Processing Systems 36, 18341-18376, 2023 | 15 | 2023 |
| Path-gradient estimators for continuous normalizing flows L Vaitl, KA Nicoli, S Nakajima, P Kessel International conference on machine learning, 21945-21959, 2022 | 15 | 2022 |
| Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse KA Nicoli, C Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... The 38th International Symposium on Lattice Field Theory, 2022 | 12 | 2022 |
| Modern applications of machine learning in quantum sciences, arXiv e-prints A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 6 | 2022 |
| Comment on" Solving Statistical Mechanics Using VANs": Introducing saVANt-VANs Enhanced by Importance and MCMC Sampling KA Nicoli, P Kessel, N Strodthoff, W Samek, KR Müller, S Nakajima arXiv preprint arXiv:1903.11048, 2019 | 4 | 2019 |
| Analysis of Atomistic Representations Using Weighted Skip-Connections KA Nicoli, P Kessel, M Gastegger, KT Schütt arXiv preprint arXiv:1810.09751, 2018 | 3 | 2018 |
| Adaptive observation cost control for variational quantum Eigensolvers CJ Anders, KA Nicoli, B Wu, N Elosegui, S Pedrielli, L Funcke, K Jansen, ... arXiv preprint arXiv:2502.01704, 2025 | 2 | 2025 |
| Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects A Bulgarelli, E Cellini, K Jansen, S Kühn, A Nada, S Nakajima, KA Nicoli, ... arXiv preprint arXiv:2410.14466, 2024 | 2 | 2024 |
| NeuLat: a toolbox for neural samplers in lattice field theories KA Nicoli, CJ Anders, L Funcke, K Jansen, S Nakajima, P Kessel The 40th International Symposium on Lattice Field Theory, 2023 | 2 | 2023 |
| Multilevel Generative Samplers for Investigating Critical Phenomena A Singha, E Cellini, KA Nicoli, K Jansen, S Kühn, S Nakajima arXiv preprint arXiv:2503.08918, 2025 | 1 | 2025 |
| Deep generative models for thermodynamics of spin systems and field theories KA Nicoli PQDT-Global, 2023 | 1 | 2023 |
| Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers S Pedrielli, CJ Anders, L Funcke, K Jansen, KA Nicoli, S Nakajima arXiv preprint arXiv:2502.02625, 2025 | | 2025 |
| Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers KA Nicoli, LJ Wagner, L Funcke arXiv preprint arXiv:2501.17689, 2025 | | 2025 |
| Simulating the Hubbard Model with Equivariant Normalizing Flows D Schuh, J Kreit, E Berkowitz, L Funcke, T Luu, KA Nicoli, M Rodekamp arXiv preprint arXiv:2501.07371, 2025 | | 2025 |
Shinichi NakajimaBIFOLD, Technische Universität BerlinVerified email at tu-berlin.de
