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 make 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 nu-cleus 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 tetra-hedral centers and settling down until the third-shell neighbor of Se atoms sited on their lattice posi-tions.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.

COPEX:co-evolutionary crystal structure prediction algorithm for complex systems

Crystal structure prediction has been widely used to accelerate the discovery of new materials in recent years.Up to this day,it remains a challenge to predict the stable stoichiometries and structures of ternary or more complex systems due to the explosive increase of the size of the chemical and configurational space.Numerous novel materials with a series of unique characteristics are expected to be found in this virgin territory while new algorithms to predict crystal structures in complex systems are urgently called for.Inspired by co-evolution in biology,here we propose a co-evolutionary algorithm,which we name COPEX,and which is based on the well-known evolutionary algorithm USPEX.Within this proposed algorithm,a few USPEX calculations for ternary systems and multiple for energetically-favored pseudobinary or fixed-composition systems are carried out in parallel,and coevolution is achieved by sharing structural information on the fittest individuals among different USPEX sub-processes during the joint evolution.We have applied the algorithm to W–Cr–B,Mg–Si–O,and Hf–Ta–C,three very different systems,and many ternary compounds have been identified.Our results clearly demonstrate that the COPEX algorithm combines efficiency and reliability even for complex systems.