| GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations Z Fan, Y Wang, P Ying, K Song, J Wang, Y Wang, Z Zeng, K Xu, ... The Journal of Chemical Physics 157 (11), 2022 | 96 | 2022 |
| Machine learning for polaritonic chemistry: Accessing chemical kinetics C Schäfer, J Fojt, E Lindgren, P Erhart Journal of the American Chemical Society, 2024 | 6 | 2024 |
| General-purpose machine-learned potential for 16 elemental metals and their alloys K Song, R Zhao, J Liu, Y Wang, E Lindgren, Y Wang, S Chen, K Xu, ... arXiv preprint arXiv:2311.04732, 2023 | 4 | 2023 |
| Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra N Xu, P Rosander, C Schäfer, E Lindgren, N Österbacka, M Fang, ... arXiv preprint arXiv:2312.05233, 2023 | 2 | 2023 |
| calorine: A Python package for constructing and sampling neuroevolution potential models E Lindgren, M Rahm, E Fransson, F Eriksson, N Österbacka, Z Fan, ... Journal of Open Source Software 9 (95), 6264, 2024 | | 2024 |
| Shedding light on liquid chromophores using machine learning E LINDGREN | | 2024 |
