Eric Lindgren

United States Air Force Research Laboratory — United States

Materials Science · Physical Sciences

6h-index603citations21works18.82yr avg
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Research Topics

Machine Learning in Materials Science(9), Neural Networks and Applications(4), Spectroscopy Techniques in Biomedical and Chemical Research(3), Ion-surface interactions and analysis(3), Neural dynamics and brain function(2)

Publications21 total

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
2025· 2 citedOpen Access
GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
Materials Genome Engineering Advances·2025· 38 citedOpen Access
Developing robust stress intensity factor models using Fourier-based data analysis to guide machine learning method selection and training
Engineering Fracture Mechanics·2025· 2 cited
Dynasor 2: From simulation to experiment through correlation functions
Computer Physics Communications·2025· 3 citedOpen Access
Probing Glass Formation in Perylene Derivatives via Atomic-Scale Simulations and Bayesian Regression
The Journal of Physical Chemistry B·2025Open Access
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
ArXiv.org·2025· 2 citedOpen Access
Developing Robust Stress Intensity Factor Models Using Fourier-Based Data Analysis to Guide Machine Learning Method Selection and Training
SSRN Electronic Journal·2025Open Access
Predicting neutron experiments from first principles: a workflow powered by machine learning
Journal of Materials Chemistry A·2025· 3 citedOpen Access
General-purpose machine-learned potential for 16 elemental metals and their alloys
Nature Communications·2024· 121 citedOpen Access
Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra
Journal of Chemical Theory and Computation·2024· 24 citedOpen Access
calorine: A Python package for constructing andsampling neuroevolution potential models
The Journal of Open Source Software·2024· 23 citedOpen Access
Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics
Journal of the American Chemical Society·2024· 26 citedOpen Access
Developing Robust Stress Intensity Factor Models Using Machine Learning: Error Sensitivity to Data Quantity, Quality, and Hyperparameter Selection
SSRN Electronic Journal·2024Open Access
General-purpose machine-learned potential for 16 elemental metals and their alloys
2023· 2 citedOpen Access
Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
arXiv (Cornell University)·2023· 2 citedOpen Access
Data for "Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra"
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Data for "Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra"
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
General-purpose machine-learned potential for 16 elemental metals and their alloys
arXiv (Cornell University)·2023· 6 citedOpen Access
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
The Journal of Chemical Physics·2022· 346 citedOpen Access
Behaviour of sulphur and nitrogen compounds measured at marine stations Lista and S�by in Scandinavia
Water Air & Soil Pollution·1995· 3 cited
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Frequent Co-authors

Paul Erhart(16), Zheyong Fan(12), Penghua Ying(7), Keke Song(7), Jiahui Liu(7), Haikuan Dong(7), Tapio Ala-Nissilä(7), Nan Xu(7), Ke Xu(6), Shunda Chen(6), Zezhu Zeng(5), Yue Chen(5), Yanjing Su(5), Esmée Berger(5), C. Schäfer(5), Nicklas Österbacka(5), Yanzhou Wang(4), Junjie Wang(4), Jian Sun(4), Jiuyang Shi(4)