Structured force reformulation of many-body dispersion: towards effective atom--atom decomposition and surrogate modeling

TL;DR

This paper introduces a structured reformulation of the many-body dispersion model, enabling physically consistent atom-atom force decomposition and laying groundwork for interpretable analysis and machine learning surrogates.

Contribution

It proposes a new force reformulation that reveals natural atom-atom interactions and supports interpretable and machine learning models for dispersion forces.

Findings

Provides a unified expression for MBD energy, force, and Hessian.

Enables physically consistent atom-atom force decomposition.

Lays foundation for interpretable analysis and surrogate modeling.

Abstract

We present a structured force reformulation of the many-body dispersion (MBD) model that enables a physically consistent decomposition of forces into pairwise components. By introducing a many-body correlation matrix that scales dipole--dipole interactions, we derive unified expressions for the MBD energy, force, and Hessian. This reformulation reveals a natural structure for effective atom--atom force decomposition and provides a promising foundation for interpretable analysis and machine learning surrogate modeling of MBD interactions.