Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915206133710848 |
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| author | Li, Yunyang Xia, Zaishuo Huang, Lin Wei, Xinran Yang, Han Harshe, Sam Wang, Zun Liu, Chang Zhang, Jia Shao, Bin Gerstein, Mark B. |
| author_facet | Li, Yunyang Xia, Zaishuo Huang, Lin Wei, Xinran Yang, Han Harshe, Sam Wang, Zun Liu, Chang Zhang, Jia Shao, Bin Gerstein, Mark B. |
| contents | Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employed deep-learning models to efficiently predict molecular and solid Hamiltonians, with roto-translational symmetries encoded in their neural networks. However, the scalability of prior models may be problematic when applied to large molecules, resulting in non-physical predictions of ground-state properties. In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy. For our model, we introduce a loss function derived from physical principles, which we call Wavefunction Alignment Loss (WALoss). WALoss involves performing a basis change on the predicted Hamiltonian to align it with the observed one; thus, the resulting differences can serve as a surrogate for orbital energy differences, allowing models to make better predictions for molecular orbitals and total energies than previously possible. WALoss also substantially accelerates self-consistent-field (SCF) DFT calculations. Here, we show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18%. These substantial improvements set new benchmarks for achieving accurate and applicable predictions in larger molecular systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems Li, Yunyang Xia, Zaishuo Huang, Lin Wei, Xinran Yang, Han Harshe, Sam Wang, Zun Liu, Chang Zhang, Jia Shao, Bin Gerstein, Mark B. Chemical Physics Materials Science Artificial Intelligence Machine Learning Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employed deep-learning models to efficiently predict molecular and solid Hamiltonians, with roto-translational symmetries encoded in their neural networks. However, the scalability of prior models may be problematic when applied to large molecules, resulting in non-physical predictions of ground-state properties. In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy. For our model, we introduce a loss function derived from physical principles, which we call Wavefunction Alignment Loss (WALoss). WALoss involves performing a basis change on the predicted Hamiltonian to align it with the observed one; thus, the resulting differences can serve as a surrogate for orbital energy differences, allowing models to make better predictions for molecular orbitals and total energies than previously possible. WALoss also substantially accelerates self-consistent-field (SCF) DFT calculations. Here, we show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18%. These substantial improvements set new benchmarks for achieving accurate and applicable predictions in larger molecular systems. |
| title | Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems |
| topic | Chemical Physics Materials Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.19227 |