Eric Lindgren

Doctoral Student at Condensed Matter and Materials Theory

Source: chalmers.se

Contact Details ORCID

Publications (10)
Projects (0)

Showing 10 publications

2025

Dynasor 2: From simulation to experiment through correlation functions

Esmée Berger, Erik Fransson, Fredrik Eriksson et al

Computer Physics Communications. Vol. 316

Journal article

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2025

Probing Glass Formation in Perylene Derivatives via Atomic-Scale Simulations and Bayesian Regression

Eric Lindgren, Jan Swenson, Christian Müller et al

Journal of Physical Chemistry B. Vol. 129 (26), p. 6613-6619

Journal article

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2025

GPUMD 4.0: A high-performance molecular dynamics package for versatile materials simulations with machine-learned potentials

Ke Xu, Hekai Bu, Shuning Pan et al

MATERIALS GENOME ENGINEERING ADVANCES. Vol. 3 (3)

Review article

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2025

Predicting neutron experiments from first principles: a workflow powered by machine learning

Eric Lindgren, Adam J. Jackson, Erik Fransson et al

Journal of Materials Chemistry A. Vol. 13 (31), p. 25509-25520

Journal article

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2024

Shedding light on liquid chromophores using machine learning

Eric Lindgren

Licentiate thesis

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2024

calorine: A Python package for constructing and sampling neuroevolution potential models

Eric Lindgren, Magnus Rahm, Erik Fransson et al

Journal of Open Source Software. Vol. 9 (95), p. 6264-6264

Journal article

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2024

General-purpose machine-learned potential for 16 elemental metals and their alloys

Keke Song, Rui Zhao, Jiahui Liu et al

Nature Communications. Vol. 15 (1)

Journal article

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2024

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

Nan Xu, Petter Rosander, Christian Schäfer et al

Journal of Chemical Theory and Computation. Vol. 20 (8), p. 3273-3284

Journal article

2024

Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics

Christian Schäfer, Jakub Fojt, Eric Lindgren et al

Journal of the American Chemical Society. Vol. 146 (8), p. 5402-5413

Journal article

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2022

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Zheyong Fan, Yanzhou Wang, Penghua Ying et al

Journal of Chemical Physics. Vol. 157 (11)

Journal article

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