Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials
Khalid Zobaid Adnan, Mahesh R. Neupane, Tianli Feng

TL;DR
This study develops machine learning interatomic potentials to accurately predict thermal boundary conductance at metal diamond interfaces, revealing limitations of traditional models and highlighting key material properties influencing heat transfer.
Contribution
The paper introduces a novel machine learning approach for predicting TBC at metal diamond interfaces, improving accuracy over classical models and providing insights into material property correlations.
Findings
Predicted TBC values for Al, Zr, Mo, Au-diamond interfaces.
Traditional models largely misestimate TBC due to neglecting inelastic and structural effects.
TBC correlates more strongly with the metal's phonon specific heat than elastic modulus.
Abstract
Thermal boundary conductance (TBC) across metal diamond interfaces plays a critical role in the thermal management of future diamond based ultrawide bandgap semiconductor devices. Molecular dynamics is a sophisticated method to predict TBC but is limited by the lack of reliable potential describing metal diamond interfaces. In this work, we report the development of machine learning interatomic potentials and the prediction of TBCs of several technologically promising metal diamond interfaces using nonequilibrium molecular dynamics. The predicted TBCs of Al, Zr, Mo, and Au-diamond interfaces are approximately 316, 88, 52, and 55 MW/m2K, respectively, after quantum corrections. The corresponding thermal boundary resistances are equivalent to 0.8 {\mu}m thick of Al, 1.4 {\mu}m Mo, 0.3 {\mu}m Zr, and 5.3 {\mu}m Au, respectively. We also find that the conventional simple models, such as the…
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Taxonomy
TopicsMachine Learning in Materials Science · Diamond and Carbon-based Materials Research · Electronic and Structural Properties of Oxides
