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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
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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