MBD-ML: Many-body dispersion from machine learning for molecules and materials
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arXiv
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| Format: | Preprint |
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2026
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| author | Moerman, Evgeny Kabylda, Adil Khabibrakhmanov, Almaz Tkatchenko, Alexandre |
| author_facet | Moerman, Evgeny Kabylda, Adil Khabibrakhmanov, Almaz Tkatchenko, Alexandre |
| contents | Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design and catalysis to battery applications. These omnipresent interactions must also be accurately included in machine-learned force fields. The many-body dispersion (MBD) method stands out as one of the most accurate and transferable approaches to capture vdW interactions, requiring only atomic $C_6$ coefficients and polarizabilities as input. We present MBD-ML, a pretrained message passing neural network that predicts these atomic properties directly from atomic structures. Through seamless integration with libMBD, our method enables the immediate calculation of MBD-inclusive total energies, forces, and stress tensors. By eliminating the need for intermediate electronic structure calculations, MBD-ML offers a practical and streamlined tool that simplifies the incorporation of state-of-the-art vdW interactions into any electronic structure code, as well as empirical and machine-learned force fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22086 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MBD-ML: Many-body dispersion from machine learning for molecules and materials Moerman, Evgeny Kabylda, Adil Khabibrakhmanov, Almaz Tkatchenko, Alexandre Chemical Physics Materials Science Machine Learning Computational Physics Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design and catalysis to battery applications. These omnipresent interactions must also be accurately included in machine-learned force fields. The many-body dispersion (MBD) method stands out as one of the most accurate and transferable approaches to capture vdW interactions, requiring only atomic $C_6$ coefficients and polarizabilities as input. We present MBD-ML, a pretrained message passing neural network that predicts these atomic properties directly from atomic structures. Through seamless integration with libMBD, our method enables the immediate calculation of MBD-inclusive total energies, forces, and stress tensors. By eliminating the need for intermediate electronic structure calculations, MBD-ML offers a practical and streamlined tool that simplifies the incorporation of state-of-the-art vdW interactions into any electronic structure code, as well as empirical and machine-learned force fields. |
| title | MBD-ML: Many-body dispersion from machine learning for molecules and materials |
| topic | Chemical Physics Materials Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2602.22086 |