Revealing Competing Kinetic Pathways in Amphiphilic Pt(II) Complex Self-Assembly via Deep Learning with Graph Neural Networks

Supramolecular assembly is a versatile bottom-up strategy for creating advanced functional materials. Metallic platinum-platinum (Pt···Pt) interactions provide a distinctive driving force for supramolecular assembly due to their strong, directional, and long-range nature. Despite their importance, the microscopic dynamics underlying the self-assembly of Pt(II) complexes remain challenging to probe experimentally. Molecular dynamics (MD) simulations can capture these processes at atomic resolution, but extracting kinetic pathways is complicated by the indistinguishability and permutation of identical monomers within self-assembled structures. In this study, we employ GraphVAMPnet, a deep learning framework based on graph neural networks (GNN), on extensive MD simulations of amphiphilic PtB complexes during the early stage of self-assembly. GraphVAMPnet inherently accounts for permutational, rotational, and translational invariance, making it well suited for analyzing self-assembly dynamics. Our analysis reveals three slow collective variables (CVs) that govern PtB self-assembly. The slowest mode (CV1) separates two distinct kinetic growth routes: an incremental growth mechanism, in which single monomers join existing aggregates with predominantly anti-parallel packing (CV3), and a hopping growth mechanism, in which clusters of smaller size merge via heterogeneous collisions, yielding a mix of anti-parallel and parallel packing arrangements (CV2). Further energetic analysis indicates that incremental growth is favored, potentially leading to the well-ordered nanosheet morphologies observed experimentally. Our findings provide molecular-level insight into PtB self-assembly pathways and showcase the capability of GraphVAMPnet in dissecting the complex dynamics of supramolecular assembly.