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Heat flux for semilocal machine-learning potentials

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Langer,  Marcel Florin       
NOMAD, Fritz Haber Institute, Max Planck Society;

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Knoop,  Florian       
NOMAD, Fritz Haber Institute, Max Planck Society;

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Carbogno,  Christian       
NOMAD, Fritz Haber Institute, Max Planck Society;

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Scheffler,  Matthias       
NOMAD, Fritz Haber Institute, Max Planck Society;

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Rupp,  Matthias       
NOMAD, Fritz Haber Institute, Max Planck Society;

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PhysRevB.108.L100302.pdf
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Citation

Langer, M. F., Knoop, F., Carbogno, C., Scheffler, M., & Rupp, M. (2023). Heat flux for semilocal machine-learning potentials. Physical Review B, 108(10): L100302. doi:10.1103/PhysRevB.108.L100302.


Cite as: https://hdl.handle.net/21.11116/0000-000E-525D-E
Abstract
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this Letter, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semilocal interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures.