Unveiling the crystallization mechanism of cadmium selenide via molecular dynamics simulation with machine-learning-based deep potential

Cadmium selenide (CdSe) is an inorganic semiconductor with unique optical and electronic properties that made it useful in various applications, including solar cells, light-emitting diodes, and biofluorescent tagging. In order to synthesize high-quality crystals and subsequently integrate them into devices, it is crucial to understand the atomic scale crystallization mechanism of CdSe. Unfortunately, such studies are still absent in the literature. To overcome this limitation, we employed an enhanced sampling-accelerated active learning approach to construct a deep neural potential with ab initio accuracy for studying the crystallization of CdSe. Our brute-force molecular dynamics simulations revealed that a spherical-like nucleus formed spontaneously and stochastically, resulting in a stacking disordered structure where the competition between hexagonal wurtzite and cubic zinc blende polymorphs is temperature-dependent. We found that pure hexagonal crystal can only be obtained approximately above 1430 K, which is 35 K below its melting temperature. Furthermore, we observed that the solidification dynamics of Cd and Se atoms were distinct due to their different diffusion coefficients. The solidification process was initiated by lower mobile Se atoms forming tetrahedral frameworks, followed by Cd atoms occupying these tetrahedral centers and settling down until the third-shell neighbor of Se atoms sited on their lattice positions. Therefore, the medium-range ordering of Se atoms governs the crystallization process of CdSe. Our findings indicate that understanding the complex dynamical process is the key to comprehending the crystallization mechanism of compounds like CdSe, and can shed lights in the synthesis of high-quality crystals.