A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910900177338368 |
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| author | Liang, Xinyuan Liu, Renxi Chen, Mohan |
| author_facet | Liang, Xinyuan Liu, Renxi Chen, Mohan |
| contents | Accurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a critical trade-off between computational cost and efficiency. We present an enhanced machine-learning Deep Kohn-Sham (DeePKS) method for improved electronic structure, DeePKS-ES, that overcomes this dilemma. By incorporating the Hamiltonian matrix and their eigenvalues and eigenvectors into the loss function, we establish a universal model for water systems, which can reproduce high-level hybrid functional (HSE06) electronic properties from inexpensive generalized gradient approximation (PBE) calculations. Validated across molecular clusters and liquid-phase simulations, our approach reliably predicts key electronic structure properties such as band gaps and density of states, as well as total energy and atomic forces. This work bridges quantum-mechanical precision with scalable computation, offering transformative opportunities for modeling aqueous systems in catalysis, climate science, and energy storage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model Liang, Xinyuan Liu, Renxi Chen, Mohan Chemical Physics Atomic and Molecular Clusters Computational Physics Accurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a critical trade-off between computational cost and efficiency. We present an enhanced machine-learning Deep Kohn-Sham (DeePKS) method for improved electronic structure, DeePKS-ES, that overcomes this dilemma. By incorporating the Hamiltonian matrix and their eigenvalues and eigenvectors into the loss function, we establish a universal model for water systems, which can reproduce high-level hybrid functional (HSE06) electronic properties from inexpensive generalized gradient approximation (PBE) calculations. Validated across molecular clusters and liquid-phase simulations, our approach reliably predicts key electronic structure properties such as band gaps and density of states, as well as total energy and atomic forces. This work bridges quantum-mechanical precision with scalable computation, offering transformative opportunities for modeling aqueous systems in catalysis, climate science, and energy storage. |
| title | A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model |
| topic | Chemical Physics Atomic and Molecular Clusters Computational Physics |
| url | https://arxiv.org/abs/2503.24050 |