Heat flux for semi-local machine-learning potentials

• URL: http://arxiv.org/abs/2303.14434v2
• Date: Tue, 28 Mar 2023 11:52:55 GMT
• Title: Heat flux for semi-local machine-learning potentials
• Authors: Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler and Matthias Rupp
• Abstract summary: The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 paper, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semi-local 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.

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