Modeling Dual-Range Atomic Interactions with Physicochemical Principles for Molecular Force Fields.

MOTIVATION: Machine Learning Force Fields (MLFFs) have emerged as promising tools for accelerating molecular dynamics simulations. However, existing approaches often struggle to capture the geometric characteristics of long-range interactions, including distance and direction, remain sensitive to conformational variations, and lack adaptive mechanisms for balancing short- and long-range forces. To address these limitations, we propose GeoNet , a physicochemical-principle-guided framework for modeling dual-range atomic interactions. GeoNet employs geometric attention over atom-fragment bipartite graphs to characterize long-range dependencies, introduces dual-level augmentation to enforce semantic consistency across molecular conformations, and uses an adaptive fusion module to dynamically balance short- and long-range interaction pathways according to local atomic environments. RESULTS: Extensive experiments show that GeoNet consistently outperforms ten state-of-the-art baselines across the evaluated benchmarks. Moreover, it achieves the smallest model size and the shortest training time, demonstrating both superior predictive performance and computational efficiency. AVAILABILITY: The source code is publicly available at https://github.com/XMUDM/GeoNet.