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  • Open Access

MBD-ML: Many-Body Dispersion from Machine Learning for Molecules and Materials

Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov, and Alexandre Tkatchenko*

  • *Contact author: alexandre.tkatchenko@uni.lu
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PRX Intelligence 1, 013003 – Published 28 July, 2026

DOI: https://doi.org/10.1103/pv86-l9h7

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 (ML) 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 C6 coefficients and polarizabilities as input. We present MBD-ML, a pretrained message-passing neural network that predicts these atomic properties directly from structures with demonstrated transferability across molecular systems and organic condensed phases. 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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PRX Intelligence 1, 013003– Published 28 July, 2026
  • Received 2 March 2026
  • Accepted 10 June 2026
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