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Heat flux for semilocal machine-learning potentials

Marcel F. Langer ^1,2,3,*, Florian Knoop ^4,3,†, Christian Carbogno ³, Matthias Scheffler³, and Matthias Rupp ^3,5,6

¹Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
²Berlin Institute for the Foundations of Learning and Data, 10623 Berlin, Germany
³The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS Adlershof of the Humboldt Universität zu Berlin, 14195 Berlin, Germany
⁴Theoretical Physics Division, Department of Physics, Chemistry and Biology (IFM), Linköping University, 581 83 Linköping, Sweden
⁵Department of Computer and Information Science, University of Konstanz, 78464 Konstanz, Germany
⁶Materials Research and Technology Department, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg

^*Corresponding author: mail@marcel.science
^†Corresponding author: florian.knoop@liu.se

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Articles Citing

Heat flux for semilocal machine-learning potentials

Beyond Boltzmann transport: Green–Kubo prediction of lattice thermal conductivity with machine-learned potentials

The quantum method of planes-local pressure definitions for machine learning potentials

Effects of phase transitions and oxygen vacancies on thermal transport in yttrium-stabilized zirconia studied using neural network potentials

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Thermal transport of glasses via machine learning driven simulations

Data Generation for Machine Learning Interatomic Potentials and Beyond

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Robustness of Local Predictions in Atomistic Machine Learning Models

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