MBD-ML: Many-body dispersion from machine learning for molecules and materials

• URL: http://arxiv.org/abs/2602.22086v1
• Date: Wed, 25 Feb 2026 16:34:53 GMT
• Title: MBD-ML: Many-body dispersion from machine learning for molecules and materials
• Authors: Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov, Alexandre Tkatchenko,
• Abstract summary: Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design to battery applications.<n>The many-body dispersion (MBD) method stands out as one of the most accurate and transferable approaches to capture vdW interactions.<n>We present MBD-ML, a pretrained message passing neural network predicts these atomic properties directly from atomic structures.
• Score: 39.27725073249277
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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