Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917376459538432 |
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| author | Qian, Chen Vitartas, Valdas Kermode, James Maurer, Reinhard J. |
| author_facet | Qian, Chen Vitartas, Valdas Kermode, James Maurer, Reinhard J. |
| contents | Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant $O(3)$ irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to $f$ orbital matrix interaction blocks. We demonstrate the model's accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing Qian, Chen Vitartas, Valdas Kermode, James Maurer, Reinhard J. Materials Science Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant $O(3)$ irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to $f$ orbital matrix interaction blocks. We demonstrate the model's accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening. |
| title | Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing |
| topic | Materials Science |
| url | https://arxiv.org/abs/2508.15108 |