Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910499940073472 |
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| author | Tang, Hao Xiao, Brian He, Wenhao Subasic, Pero Harutyunyan, Avetik R. Wang, Yao Liu, Fang Xu, Haowei Li, Ju |
| author_facet | Tang, Hao Xiao, Brian He, Wenhao Subasic, Pero Harutyunyan, Avetik R. Wang, Yao Liu, Fang Xu, Haowei Li, Ju |
| contents | Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12229 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy Tang, Hao Xiao, Brian He, Wenhao Subasic, Pero Harutyunyan, Avetik R. Wang, Yao Liu, Fang Xu, Haowei Li, Ju Chemical Physics Materials Science Artificial Intelligence Computational Engineering, Finance, and Science Computational Physics Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods. |
| title | Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy |
| topic | Chemical Physics Materials Science Artificial Intelligence Computational Engineering, Finance, and Science Computational Physics |
| url | https://arxiv.org/abs/2405.12229 |