First-principles calculations reveal thermal conductivity mechanisms in CuCl, suggesting machine learning enhances accuracy in modeling.
Accurate prediction of lattice thermal conductivity <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mo>(</a:mo> <a:msub> <a:mi>κ</a:mi> <a:mi>l</a:mi> </a:msub> <a:mo>)</a:mo> </a:math> in strongly anharmonic materials requires renormalized interatomic force constants (IFCs) and appropriate incorporation of diagonal and off-diagonal contributions and higher-order scattering. We investigate CuCl, a highly anharmonic system with a simple zincblende structure and ultralow <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"> <b:msub> <b:mi>κ</b:mi> <b:mi>l</b:mi> </b:msub> </b:math> . Our calculations, including IFC renormalization and four-phonon scattering, show excellent agreement with the experiment, underscoring the critical role of both effects in the accurate estimation of <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:msub> <c:mi>κ</c:mi> <c:mi>l</c:mi> </c:msub> </c:math> . Furthermore, the unusual pressure dependence of <d:math xmlns:d="http://www.w3.org/1998/Math/MathML"> <d:msub> <d:mi>κ</d:mi> <d:mi>l</d:mi> </d:msub> </d:math> is explored using a rigorously validated machine-learned force field, with the predicted values showing good agreement with the experimentally observed trend of monotonic decrease. This behavior is primarily driven by a significant increase in four-phonon scattering and a reduction in the group velocity of transverse acoustic modes. Overall, this study establishes a robust framework for modeling thermal transport in strongly anharmonic materials.
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Kundu et al. (2025) studied this question.
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