| 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 | 179 | 2022 |
| 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, ... Nature Communications 15 (1), 10208, 2024 | 27 | 2024 |
| Machine learning for polaritonic chemistry: Accessing chemical kinetics C Schafer, J Fojt, E Lindgren, P Erhart Journal of the American Chemical Society 146 (8), 5402-5413, 2024 | 16 | 2024 |
| 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, ... Journal of Chemical Theory and Computation 20 (8), 3273-3284, 2024 | 13 | 2024 |
| 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 | 9 | 2024 |
| Shedding Light on Liquid Chromophores Using Machine Learning E Lindgren Department of Physics, Chalmers University of Technology, 2024 | | 2024 |
