Multi-scale Crystallization Materials: Advances in in-situ Characterization Techniques and Computational Simulations

Large-sized crystalline materials are the basic raw materials in semiconductors, lasers, and communications. Preparation of large-scale, high-quality crystalline materials has become a bottleneck restricting the development of related industries. Breaking through the preparation theory and technology of large-sized crystal materials is the key to obtaining high-quality large-sized crystals. Preparation process of crystal materials often undergoes nucleation and growth stages, including multiple processes at spatiotemporal scale: from atom/molecules, through clusters and nuclei, to bulk crystals. To further explore and accurately understand the crystal growth mechanism, we need intensively study the multiscale process,multi-scale in situ characterization techniques, and computational simulation methods. Among them, the latest in situ characterization methods for crystal growth includes optical microscopy, electron microscopy, vibration spectra, synchrotron radiation, neutron technology, and especially, machine learning method. Thus, the multi-scale computational simulation techniques for crystallization is introduced, for example, first principles calculation at atom/molecular scale, molecular dynamics simulation, Monte Carlo simulation, phase field simulation at mesoscopic scale, and finite element simulation at macroscopic scale. A single in situ characterization or simulation technique can only explore crystallization information over a specific time and space scale. To accurately and fully reflect the crystallization process, a combination of multi-scale methods is introduced. It can be speculated that the establishment of in situ characterization technology and computational simulation methods for the actual large-sized crystal growth environment will be the future development trend, which provides an important experimental and theoretical basis for developing crystallization theory and controlling crystal quality. Furthermore, it can be deduced that the combination of in situ characterization technology with machine learning and big data technology will be the trend for large-sized crystal growth.