MBD-ML: Many-body dispersion from machine learning for molecules and materials
This provides a practical tool for researchers in fields like drug design and materials science to incorporate state-of-the-art van der Waals interactions more efficiently, though it is incremental as it builds on existing MBD methods.
The researchers tackled the challenge of accurately including van der Waals interactions in computational models for molecules and materials by developing MBD-ML, a pretrained neural network that predicts atomic properties for many-body dispersion calculations, enabling immediate computation of energies and forces without intermediate electronic structure steps.
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.