Eric Lindgren

Hi! My name is Eric Lindgren, and I’m a PhD student at the Devision for Condensed Matter and Materials Theory at Chalmers. I have a B.Sc. in Engineering Physics and a MSc in Physics, both from Chalmers. I’m very interested in computational physics and modelling, using tools such as machine learning, which is something I’ve focused on during my studies.

I have been a part of SwedNess since starting my PhD studies in early autumn 2021, and my SwedNess project focuses on investigating the structural and dynamical properties of liquid chromophores. Chromophores are a class of molecules which are responsible for giving things color, with a notable example being the part of the beta-carotene molecule that makes carrots and pumpkins orange. Specifically, I develop machine learning models of the intermolecular interactions between chromophores, with the aim of using the models to efficiently probe the dynamics within these systems over large timescales. Neutron scattering plays a crucial role in my project, since it enables me to both probe the structure and dynamics of liquid chromophores (using e.g. INS & QENS), as well as being instrumental in validating my machine learning models.

University: Chalmers University of Technology
Project Title: Framework for modeling neutron spectra of liquid chromophores
E-mail: eric.lindgren@chalmers.se

Publications

Probing Glass Formation in Perylene Derivatives via Atomic Scale Simulations and Bayesian Regression. E. Lindgren, J. Swensson, C. Müller, and P. Erhart  arXiv:2501.15872 (2025) https://doi.org/10.1021/acs.jpcb.5c00837 

Predicting neutron experiments from first principles: a workflow powered by machine learning E. Lindgren,  A. J. Jackson, E.Fransson, E. Berger, G. Škoro, S. Rudić, R. Turanyi, S. Mukhopadhyay,  P.Erhart J. Mater. Chem. A (2025) 13, 25509, DOI: 10.1039/D5TA03325J

calorine: A Python package for constructing and sampling neuroevolution potential models. E. Lindgren, J. M. Rahm, E. Fransson, F. Eriksson, N. Österbacka, Z. Fan, and P. Erhart  Journal of Open Source Software 9, 6264 (2024) https://doi.org/10.21105/joss.06264

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, W. Chen, Y. He, Z. Fan, and P. Erhart, Journal of Chemical Theory and Computation 20, 3273 (2024)* https://doi.org/10.1021/acs.jctc.3c01343

Machine Learning for Polaritonic Chemistry: Accessing chemical kinetics. C. Schäfer, J. Fojt, E. Lindgren, and P. Erhart  Journal of the American Chemical Society 146, 5402 (2024) https://doi.org/10.1021/jacs.3c12829

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, T. Liang, P. Ying, N. Xu, Z. Zhao, J. Shi, J. Wang, S. Lyu, Z. Zeng, S. Liang, H. Dong, L. Sun, Y. Chen, Z. Zhang, W. Guo, P. Qian, J. Sun, P. Erhart, T. Ala-Nissila, Y. Su, and Z. Fan Nature Communications 15, 10208 (2024) https://www.nature.com/articles/s41467-024-54554-x

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, E. Lindgren, J.M. Rahm, A.J. Gabourie, J. Liu, H. Dong, J. Wu, Y. Chen, Z. Zhong, J. Sun, P. Erhart, Y. Su, T. Ala-Nissila. The Journal of Chemical Physics (2022). https://doi.org/10.1063/5.0106617