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

arXiv: https://arxiv.org/abs/2602.22086 · HTML: https://mlsanity.com/paper/2602.22086/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 76 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models](https://mlsanity.com/paper/2410.12771/) (2024)

## Abstract (arXiv)

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 C6 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.