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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05480 |
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| _version_ | 1866911044674256896 |
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| author | Hou, Bowen Xu, Xian Wu, Jinyuan Qiu, Diana Y. |
| author_facet | Hou, Bowen Xu, Xian Wu, Jinyuan Qiu, Diana Y. |
| contents | Recently, radical progress in machine learning (ML) has revolutionized computational materials science, enabling unprecedentedly rapid materials discovery and property prediction, but the quantum many-body problem -- which is the key to understanding excited-state properties, ranging from transport to optics -- remains challenging due to the complexity of the nonlocal and energy-dependent interactions. Here, we propose a symmetry-aware, grid-free, transformer-based model, MBFormer, that is designed to learn the entire many-body hierarchy directly from mean-field inputs, exploiting the attention mechanism to accurately capture many-body correlations between mean-field states. As proof of principle, we demonstrate the capability of MBFormer in predicting results based on the GW plus Bethe Salpeter equation (GW-BSE) formalism, including quasiparticle energies, exciton energies, exciton oscillator strengths, and exciton wavefunction distribution. Our model is trained on a dataset of 721 two-dimensional materials from the C2DB database, achieving state-of-the-art performance with a low prediction mean absolute error (MAE) on the order of 0.1-0.2 eV for state-level quasiparticle and exciton energies across different materials. Moreover, we show explicitly that the attention mechanism plays a crucial role in capturing many-body correlations. Our framework provides an end-to-end platform from ground states to general many-body prediction in real materials, which could serve as a foundation model for computational materials science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05480 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Hou, Bowen Xu, Xian Wu, Jinyuan Qiu, Diana Y. Materials Science Computational Physics Recently, radical progress in machine learning (ML) has revolutionized computational materials science, enabling unprecedentedly rapid materials discovery and property prediction, but the quantum many-body problem -- which is the key to understanding excited-state properties, ranging from transport to optics -- remains challenging due to the complexity of the nonlocal and energy-dependent interactions. Here, we propose a symmetry-aware, grid-free, transformer-based model, MBFormer, that is designed to learn the entire many-body hierarchy directly from mean-field inputs, exploiting the attention mechanism to accurately capture many-body correlations between mean-field states. As proof of principle, we demonstrate the capability of MBFormer in predicting results based on the GW plus Bethe Salpeter equation (GW-BSE) formalism, including quasiparticle energies, exciton energies, exciton oscillator strengths, and exciton wavefunction distribution. Our model is trained on a dataset of 721 two-dimensional materials from the C2DB database, achieving state-of-the-art performance with a low prediction mean absolute error (MAE) on the order of 0.1-0.2 eV for state-level quasiparticle and exciton energies across different materials. Moreover, we show explicitly that the attention mechanism plays a crucial role in capturing many-body correlations. Our framework provides an end-to-end platform from ground states to general many-body prediction in real materials, which could serve as a foundation model for computational materials science. |
| title | MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2507.05480 |