Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration
Abstract Machine learning offers a promising avenue for expediting the discovery of new compounds by accurately predicting their thermodynamic stability. This approach provides significant advantages in terms of time and resource efficiency compared to traditional experimental and modeling methods....
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55525-y |
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| _version_ | 1841559322324107264 |
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| author | Hao Zou Haochen Zhao Mingming Lu Jiong Wang Zeyu Deng Jianxin Wang |
| author_facet | Hao Zou Haochen Zhao Mingming Lu Jiong Wang Zeyu Deng Jianxin Wang |
| author_sort | Hao Zou |
| collection | DOAJ |
| description | Abstract Machine learning offers a promising avenue for expediting the discovery of new compounds by accurately predicting their thermodynamic stability. This approach provides significant advantages in terms of time and resource efficiency compared to traditional experimental and modeling methods. However, most existing models are constructed based on specific domain knowledge, potentially introducing biases that impact their performance. Here, we propose a machine learning framework rooted in electron configuration, further enhanced through stack generalization with two additional models grounded in diverse domain knowledge. Experimental results validate the efficacy of our model in accurately predicting the stability of compounds, achieving an Area Under the Curve score of 0.988. Notably, our model demonstrates exceptional efficiency in sample utilization, requiring only one-seventh of the data used by existing models to achieve the same performance. To underscore the versatility of our approach, we present three illustrative examples showcasing its effectiveness in navigating unexplored composition space. We present two case studies to demonstrate that our method can facilitate the exploration of new two-dimensional wide bandgap semiconductors and double perovskite oxides. Validation results from first-principles calculations indicate that our method demonstrates remarkable accuracy in correctly identifying stable compounds. |
| format | Article |
| id | doaj-art-dd68d8d02c6d4ff481f3ae2edfa785bb |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-dd68d8d02c6d4ff481f3ae2edfa785bb2025-01-05T12:37:01ZengNature PortfolioNature Communications2041-17232025-01-0116111510.1038/s41467-024-55525-yPredicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configurationHao Zou0Haochen Zhao1Mingming Lu2Jiong Wang3Zeyu Deng4Jianxin Wang5School of Computer Science and Engineering, Central South UniversitySchool of Computer Science and Engineering, Central South UniversitySchool of Computer Science and Engineering, Central South UniversityPowder Metallurgy Institute, Central South UniversityDepartment of Materials Science and Engineering, National University of SingaporeSchool of Computer Science and Engineering, Central South UniversityAbstract Machine learning offers a promising avenue for expediting the discovery of new compounds by accurately predicting their thermodynamic stability. This approach provides significant advantages in terms of time and resource efficiency compared to traditional experimental and modeling methods. However, most existing models are constructed based on specific domain knowledge, potentially introducing biases that impact their performance. Here, we propose a machine learning framework rooted in electron configuration, further enhanced through stack generalization with two additional models grounded in diverse domain knowledge. Experimental results validate the efficacy of our model in accurately predicting the stability of compounds, achieving an Area Under the Curve score of 0.988. Notably, our model demonstrates exceptional efficiency in sample utilization, requiring only one-seventh of the data used by existing models to achieve the same performance. To underscore the versatility of our approach, we present three illustrative examples showcasing its effectiveness in navigating unexplored composition space. We present two case studies to demonstrate that our method can facilitate the exploration of new two-dimensional wide bandgap semiconductors and double perovskite oxides. Validation results from first-principles calculations indicate that our method demonstrates remarkable accuracy in correctly identifying stable compounds.https://doi.org/10.1038/s41467-024-55525-y |
| spellingShingle | Hao Zou Haochen Zhao Mingming Lu Jiong Wang Zeyu Deng Jianxin Wang Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration Nature Communications |
| title | Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration |
| title_full | Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration |
| title_fullStr | Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration |
| title_full_unstemmed | Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration |
| title_short | Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration |
| title_sort | predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration |
| url | https://doi.org/10.1038/s41467-024-55525-y |
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