physics.chem-phFeb 25, 2026

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

Authors: Evgeny Moerman, Adil Kabylda, Almaz Khabibrakhmanov, Alexandre Tkatchenko

Organizations: Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg

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 C6C_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.

Figures & tables

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Explore similar work

CardsList
  1. Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials

    Mar 24, 2026Shuyu Bi, Zhede Zhao, Qiangchao Sun +3Machine Learning Interatomic PotentialsGraph Neural Networks

  2. Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations

    Jul 31, 2026Johannes Maeß, Leon Werner, J. Thorben Frank +5Implicit Neural RepresentationsMachine Learning Interatomic Potentials

  3. MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials

    Mar 2, 2026Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3Machine Learning Interatomic PotentialsComputational Materials Science