Phase Synchronization Dynamics for Molecular Property Prediction

Accurate molecular property prediction is predominantly addressed through graph representation learning. However, conventional spatial message-passing neural networks encounter inherent structural bottlenecks when modeling long-range dependencies and complex global topologies. We propose MolSync (Molecular Synchronization Networks), which represents molecular graphs as networks of non-linear phase oscillators and maps structural priors into a dynamic phase space. The resulting synchronization process provides an intrinsic feature-binding mechanism that organizes local chemical environments and higher-order structural context through learned oscillator interactions. Theoretically, we show that continuous synchronization removes the hard finite-hop dependency constraint of fixed-depth message passing, heterogeneous intrinsic dynamics sustain non-trivial representation variation at phase-locked states, and spectral evolution can distinguish a class of 1-WL-indistinguishable graphs with different weighted Laplacian spectra. Extensive experiments show strong predictive performance, while energy-based attribution and oscillator analyses reveal chemically meaningful, task-dependent patterns in the learned dynamics.