Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914194536792064 |
|---|---|
| author | Al-Fahdi, Mohammed Rurali, Riccardo Hu, Jianjun Wolverton, Christopher Hu, Ming |
| author_facet | Al-Fahdi, Mohammed Rurali, Riccardo Hu, Jianjun Wolverton, Christopher Hu, Ming |
| contents | Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized negative integrated crystal orbital Hamilton populations (-ICOHP) and normalized integrated crystal orbital bond index (ICOBI), that strongly correlate with LTC and rattling (mean-squared displacement), surpassing empirical rules and the unnormalized -ICOHP across >4,500 inorganic crystals by first-principles. We train a Crystal Attention Graph Neural Network (CATGNN) to predict these descriptors and screen ~200,000 database structures for extreme LTCs. From 367 (533) candidates with low (high) normalized -ICOHP and normalized ICOBI, first-principles validation identifies 106 dynamically stable compounds with LTC <5 W/mK (68% <2 W/mK) and 13 stable compounds with LTC >100 W/mK. The descriptors' low cost and clear physical meaning provide a rapid, reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation, thermoelectrics, and electronics cooling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors Al-Fahdi, Mohammed Rurali, Riccardo Hu, Jianjun Wolverton, Christopher Hu, Ming Materials Science Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized negative integrated crystal orbital Hamilton populations (-ICOHP) and normalized integrated crystal orbital bond index (ICOBI), that strongly correlate with LTC and rattling (mean-squared displacement), surpassing empirical rules and the unnormalized -ICOHP across >4,500 inorganic crystals by first-principles. We train a Crystal Attention Graph Neural Network (CATGNN) to predict these descriptors and screen ~200,000 database structures for extreme LTCs. From 367 (533) candidates with low (high) normalized -ICOHP and normalized ICOBI, first-principles validation identifies 106 dynamically stable compounds with LTC <5 W/mK (68% <2 W/mK) and 13 stable compounds with LTC >100 W/mK. The descriptors' low cost and clear physical meaning provide a rapid, reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation, thermoelectrics, and electronics cooling. |
| title | Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors |
| topic | Materials Science |
| url | https://arxiv.org/abs/2410.16066 |