Surface energy prediction and Winterbottom morphology evolution analysis in Winterbottom construction on various crystal orientations using machine learning

Surface energy is a crucial property in materials science, and studying Winterbottom morphologies holds significant importance. This research aims to systematically investigate the surface energy characteristics of Winterbottom construction on substrates through numerical simulations. An algorithm is implemented that constructs the Winterbottom morphology for any given crystal structure based on its preferred growth planes expressed in Miller indices and their corresponding surface energy and interfacial energy. By varying parameters and using {1 0 0} and {111} crystal facets as substrates, this work generated a diverse range of Winterbottom morphologies. In addition, we have developed a model based on the random forest regression to obtain the interfacial energy and facet -dependent surface energy from experimentally determined equilibrium shapes. Polynomial regression analysis is used to develop predictive models for surface energy. By comparing the experimental and simulation results, the accuracy and reliability of the simulation method are validated. The model's predictive capability and stability are verified through cross -validation and error analysis. Our findings indicate distinct surface energy differences between Winterbottom morphologies on different crystal facets, with a positive correlation observed between surface area and surface energy. These research outcomes contribute to a deeper understanding of surface energy characteristics in Winterbottom morphologies and provide insights for optimization. Additionally, our study offers references for the development of surface energy prediction models.