Divergence between local structure, dynamics and nucleation outcome in heterogeneous nucleation of close-packed crystals

Heterogeneous crystal nucleation is the dominant mechanism of crystallization in most systems, yet its underlying physics remains an enigma. While emergent interfacial crystalline order precedes heterogeneous nucleation, its importance in the nucleation mechanism is unclear. Here, we use molecular dynamics simulations and path sampling techniques to demonstrate that crystalline order in its traditional sense is not predictive of the outcome of heterogeneous nucleation of close-packed crystals. Consequently, structure-based collective variables (CVs) that reliably describe homogeneous nucleation can be poor descriptors of heterogeneous nucleation. This divergence between structure and nucleation outcome is accompanied by an intriguing dynamical anomaly wherein low- coordinated crystalline particles outpace their liquid-like counterparts. Both these anomalies are morphologically associated with low-coordinated crystalline particles participating in bridges connecting crystalline domains. We also demonstrate that reliance on ineffective CVs yields a flawed comprehension of the nucleation mechanism by overestimating the face-centered cubic (FCC) content of crystalline nuclei in the systems and surfaces considered here. We use committor analysis, high-throughput screening, and machine learning to devise CV optimization strategies, and present suitable structural heuristics within the metastable fluid for CV pre-screening. Employing such optimized CVs is pivotal in properly characterizing the mechanism of heterogeneous nucleation in a wide variety of systems, including inorganic, metallic and colloidal systems.