Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials

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Understanding the thermal transport properties of CALF-20, a recent addition to the metal-organic framework family, is crucial for its effective utilization in greenhouse gas capture. Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. We use the Green-Kubo approach based on equilibrium molecular dynamics, with a heat-flux renormalization technique, to determine the thermal conductivity (κ) of CALF-20. We predict that the anisotropic thermal transport in CALF-20, with κ below 1 Wm−1K−1 at 300 K, is ideal for thermoelectric applications. Our analysis reveals a weak temperature dependence (κ ~ 1/T0.56) and near invariance with pressure in κ value of CALF-20, which stands out from the typical trend observed in crystalline materials. The outcome of the study, leveraging advanced computational techniques for predictive modeling, offers valuable insights into more suitable applications of CALF-20 with tailored thermal properties.