Self-consistent Validation for Machine Learning Electronic Structure
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916126886199296 |
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| author | Hu, Gengyuan Wei, Gengchen Lou, Zekun Torr, Philip H. S. Ouyang, Wanli Zhong, Han-sen Lin, Chen |
| author_facet | Hu, Gengyuan Wei, Gengchen Lou, Zekun Torr, Philip H. S. Ouyang, Wanli Zhong, Han-sen Lin, Chen |
| contents | Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has been proposed to estimate the accuracy of the predictions. This method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model's ability with active learning and instills confidence in its integration into real-world studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-consistent Validation for Machine Learning Electronic Structure Hu, Gengyuan Wei, Gengchen Lou, Zekun Torr, Philip H. S. Ouyang, Wanli Zhong, Han-sen Lin, Chen Machine Learning Chemical Physics Computational Physics Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has been proposed to estimate the accuracy of the predictions. This method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model's ability with active learning and instills confidence in its integration into real-world studies. |
| title | Self-consistent Validation for Machine Learning Electronic Structure |
| topic | Machine Learning Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2402.10186 |