Alexander Lindmaa

Materials Science · Physical Sciences

6h-index931citations17works0.02yr avg
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Research Topics

Machine Learning in Materials Science(6), Advanced Chemical Physics Studies(5), X-ray Diffraction in Crystallography(5), Spectroscopy and Quantum Chemical Studies(4), Quantum, superfluid, helium dynamics(3)

Publications17 total

Semi-Local Parameterization of the Electron Localization Function in Second-Order Density Gradients
arXiv (Cornell University)·2019· 1 citedOpen Access
FLLA Data
Figshare·2018Open Access
FLLA Data
Figshare·2018Open Access
Theoretical prediction of properties of atomistic systems : Density functional theory and machine learning
Linköping studies in science and technology. Dissertations·2017· 1 citedOpen Access
Modeling of the exact exchange derivative discontinuity in semi-local potentials
Bulletin of the American Physical Society·2017
Erratum: Quantum oscillations in the kinetic energy density: Gradient corrections from the Airy gas [Phys. Rev. B <b>90</b>, 075139 (2014)]
Physical review. B./Physical review. B·2017· 2 citedOpen Access
Energetics of the AK13 semilocal Kohn-Sham exchange energy functional
Physical review. B./Physical review. B·2016· 12 citedOpen Access
Machine Learning Energies of 2 Million Elpasolite<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>A</mml:mi><mml:mi>B</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math>Crystals
Physical Review Letters·2016· 392 citedOpen Access
Machine Learning Energies of 2 M Elpasolite (ABC$_2$D$_6$) Crystals
arXiv (Cornell University)·2015· 1 citedOpen Access
Crystal structure representations for machine learning models of formation energies
International Journal of Quantum Chemistry·2015· 450 citedOpen Access
Crystal Structure Representations for Machine Learning Models of\n Formation Energies
arXiv (Cornell University)·2015· 1 citedOpen Access
Accurate Energies and Orbital Description in Semi-Local Kohn-Sham DFT
Bulletin of the American Physical Society·2015
Crystal Structure Representations for Machine Learning Models of Formation Energies
arXiv (Cornell University)·2015· 18 citedOpen Access
Quantum oscillations in the kinetic energy density: Gradient corrections from the Airy gas
Physical Review B·2014· 24 citedOpen Access
Quantum oscillations in the kinetic energy density: Gradient corrections from the Airy gas
arXiv (Cornell University)·2014Open Access
Exchange interactions in paramagnetic amorphous and disordered crystalline<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>CrN</mml:mi></mml:math>-based systems
Physical Review B·2013· 29 citedOpen Access
A Kinetic Energy Functional From the Airy Gas Model
Bulletin of the American Physical Society·2013

Frequent Co-authors

Rickard Armiento(15), Felix A. Faber(7), O. Anatole von Lilienfeld(5), Ann E. Mattsson(5), Anatole von Lilienfeld(2), Hacking Materials(2), Raquel Lizárraga(1), Erik Holmström(1), Igor A. Abrikosov(1), Björn Alling(1), Joel Davidsson(1), Stephan Kuemmel(1)