Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials
Abstract 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 learni...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-025-00745-y |
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| author | Soham Mandal Prabal K. Maiti |
| author_facet | Soham Mandal Prabal K. Maiti |
| author_sort | Soham Mandal |
| collection | DOAJ |
| description | Abstract 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/T 0.56) 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. |
| format | Article |
| id | doaj-art-ae5b68b6d23e4f05b7f99cf61e072002 |
| institution | Kabale University |
| issn | 2662-4443 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Materials |
| spelling | doaj-art-ae5b68b6d23e4f05b7f99cf61e0720022025-02-02T12:34:23ZengNature PortfolioCommunications Materials2662-44432025-02-016111210.1038/s43246-025-00745-yPrediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentialsSoham Mandal0Prabal K. Maiti1Centre for Condensed Matter Theory, Department of Physics, Indian Institute of ScienceCentre for Condensed Matter Theory, Department of Physics, Indian Institute of ScienceAbstract 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/T 0.56) 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.https://doi.org/10.1038/s43246-025-00745-y |
| spellingShingle | Soham Mandal Prabal K. Maiti Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials Communications Materials |
| title | Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials |
| title_full | Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials |
| title_fullStr | Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials |
| title_full_unstemmed | Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials |
| title_short | Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials |
| title_sort | prediction of thermal conductivity in calf 20 with first principles accuracy via machine learning interatomic potentials |
| url | https://doi.org/10.1038/s43246-025-00745-y |
| work_keys_str_mv | AT sohammandal predictionofthermalconductivityincalf20withfirstprinciplesaccuracyviamachinelearninginteratomicpotentials AT prabalkmaiti predictionofthermalconductivityincalf20withfirstprinciplesaccuracyviamachinelearninginteratomicpotentials |