PINK: physical-informed machine learning for lattice thermal conductivity
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915208208842752 |
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| author | Liu, Yujie Wang, Xiaoying Hao, Yuzhou Li, Xuejie Sun, Jun Lookman, Turab Ding, Xiangdong Gao, Zhibin |
| author_facet | Liu, Yujie Wang, Xiaoying Hao, Yuzhou Li, Xuejie Sun, Jun Lookman, Turab Ding, Xiangdong Gao, Zhibin |
| contents | Lattice thermal conductivity ($κ_L$) is crucial for efficient thermal management in electronics and energy conversion technologies. Traditional methods for predicting \k{appa}L are often computationally expensive, limiting their scalability for large-scale material screening. Empirical models, such as the Slack model, offer faster alternatives but require time-consuming calculations for key parameters such as sound velocity and the Gruneisen parameter. This work presents a high-throughput framework, physical-informed kappa (PINK), which combines the predictive power of crystal graph convolutional neural networks (CGCNNs) with the physical interpretability of the Slack model to predict \k{appa}L directly from crystallographic information files (CIFs). Unlike previous approaches, PINK enables rapid, batch predictions by extracting material properties such as bulk and shear modulus from CIFs using a well-trained CGCNN model. These properties are then used to compute the necessary parameters for $κ_L$ calculation through a simplified physical formula. PINK was applied to a dataset of 377,221 stable materials, enabling the efficient identification of promising candidates with ultralow $κ_L$ values, such as Ag$_3$Te$_4$W and Ag$_3$Te$_4$Ta. The platform, accessible via a user-friendly interface, offers an unprecedented combination of speed, accuracy, and scalability, significantly accelerating material discovery for thermal management and energy conversion applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PINK: physical-informed machine learning for lattice thermal conductivity Liu, Yujie Wang, Xiaoying Hao, Yuzhou Li, Xuejie Sun, Jun Lookman, Turab Ding, Xiangdong Gao, Zhibin Materials Science Mesoscale and Nanoscale Physics Applied Physics Computational Physics Lattice thermal conductivity ($κ_L$) is crucial for efficient thermal management in electronics and energy conversion technologies. Traditional methods for predicting \k{appa}L are often computationally expensive, limiting their scalability for large-scale material screening. Empirical models, such as the Slack model, offer faster alternatives but require time-consuming calculations for key parameters such as sound velocity and the Gruneisen parameter. This work presents a high-throughput framework, physical-informed kappa (PINK), which combines the predictive power of crystal graph convolutional neural networks (CGCNNs) with the physical interpretability of the Slack model to predict \k{appa}L directly from crystallographic information files (CIFs). Unlike previous approaches, PINK enables rapid, batch predictions by extracting material properties such as bulk and shear modulus from CIFs using a well-trained CGCNN model. These properties are then used to compute the necessary parameters for $κ_L$ calculation through a simplified physical formula. PINK was applied to a dataset of 377,221 stable materials, enabling the efficient identification of promising candidates with ultralow $κ_L$ values, such as Ag$_3$Te$_4$W and Ag$_3$Te$_4$Ta. The platform, accessible via a user-friendly interface, offers an unprecedented combination of speed, accuracy, and scalability, significantly accelerating material discovery for thermal management and energy conversion applications. |
| title | PINK: physical-informed machine learning for lattice thermal conductivity |
| topic | Materials Science Mesoscale and Nanoscale Physics Applied Physics Computational Physics |
| url | https://arxiv.org/abs/2503.17060 |