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[2602.22086] MBD-ML: Many-body dispersion from machine learning for molecules and materials
Summary
The paper presents MBD-ML, a machine learning model that predicts many-body dispersion interactions in molecules and materials, enhancing computational efficiency in electronic structure calculations.
Why It Matters
Accurate modeling of van der Waals interactions is crucial for various applications in chemistry and materials science. MBD-ML simplifies the integration of these interactions into computational models, making advanced simulations more accessible and efficient for researchers.
Key Takeaways
- MBD-ML predicts atomic properties essential for many-body dispersion.
- The model integrates seamlessly with existing electronic structure codes.
- Eliminates the need for intermediate electronic structure calculations.
- Facilitates accurate modeling of van der Waals interactions.
- Offers a practical tool for researchers in chemistry and materials science.
Physics > Chemical Physics arXiv:2602.22086 (physics) [Submitted on 25 Feb 2026] Title:MBD-ML: Many-body dispersion from machine learning for molecules and materials Authors:Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov, Alexandre Tkatchenko View a PDF of the paper titled MBD-ML: Many-body dispersion from machine learning for molecules and materials, by Evgeny Moerman and 3 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Chemical Physics (physics.che...
