mlsanityData to 14 Oct 2026

Heat flux for semi-local machine-learning potentials

arXiv 2303.14434 ↗First posted 25 Mar 2023cond-mat.mtrl-sci cs.LG physics.comp-phcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 68 references that are in the map. First posted on arXiv 25 Mar 2023.

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8 of 68 references.

References · 8

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  3. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  4. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
  5. 2020Machine Learning Force Fieldscited by 207
  6. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  7. 2022The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentialscited by 101
  8. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Stress and heat flux via automatic differentiationcited by 1
  2. 2023From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fieldscited by 1
  3. 2024Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learningcited by 1