Eric Lindgren

Research

Eric’s research is concerned with the development and application of a computational framework that combines atomic-scale modeling, machine learning, and neutron scattering data in order to extract structural and dynamical information of liquid chromophores in both their ground and excited states. His Ph.D. project is part of the SwedNess research school.

Education

• 2024: Licentiate in Physics; Chalmers University of Technology
• 2021: M.Sc. in Physics; Chalmers University of Technology
  Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction
• 2019: B.Sc. in Applied Physics; Chalmers University of Technology

Publications

• Probing Glass Formation in Perylene Derivatives via Atomic Scale Simulations and Bayesian Regression Permalink
  E. Lindgren, J. Swensson, C. Müller, and P. Erhart
  arXiv:2501.15872 (2025)
• Dynasor 2: From Simulation to Experiment Through Correlation Functions Permalink
  E. Berger, E. Fransson, F. Eriksson, E. Lindgren, G. Wahnström, T. H. Rod, and P. Erhart
  arXiv:2503.21957 (2025)
• calorine: A Python package for constructing and sampling neuroevolution potential models Permalink
  E. Lindgren, J. M. Rahm, E. Fransson, F. Eriksson, N. Österbacka, Z. Fan, and P. Erhart
  Journal of Open Source Software 9, 6264 (2024)
• Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra Permalink
  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)
• Machine Learning for Polaritonic Chemistry: Accessing chemical kinetics Permalink
  C. Schäfer, J. Fojt, E. Lindgren, and P. Erhart
  Journal of the American Chemical Society 146, 5402 (2024)
• General-purpose machine-learned potential for 16 elemental metals and their alloys Permalink
  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)
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations Permalink
  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, and T. Ala-Nissila
  Journal of Chemical Physics 157, 114801 (2022)

Theses

• Shedding light on liquid chromophores using machine learning Permalink
  E. Lindgren, Licentiate Thesis (2024)