Bonding-Aware Representation Learning for Machine-Learning Force Fields

Machine-learning force fields (MLFFs) provide a promising framework for modeling molecular energies and forces from atomic configurations. However, most existing MLFFs construct representations from local geometric neighborhoods defined by a fixed cutoff, treating nearby atom pairs primarily through their relative positions. Such representations do not explicitly encode bond connectivity or continuous changes in bond state during dynamics, e.g., reactive dynamics. Consequently, they may struggle to capture the associated variations in energy and force, particularly when the accompanying charge redistribution must satisfy global charge conservation. To address this challenge, we introduce a bond-aware representation framework that models local atom pairs as nodes in a bipartite bond-incidence graph. Each pair is assigned a structured state with orientation-invariant and orientation-equivariant components: the former captures latent bonding information, while the latter encodes signed charge-transfer flow. Aggregation through a signed incidence matrix produces orientation-independent atomic charges while exactly conserving the prescribed total charge. We evaluate our method on rMD17, MD22, Chignolin, Transition1x, and HORM, covering both non-equilibrium and reactive molecular dynamics. Across these benchmarks, our bond-aware model improves energy and force prediction accuracy. Stability analyses and assessments of energy drift further demonstrate the performance of our model in molecular dynamics simulations.