Artificial Neural Network-Based Prediction and Morphological Evolution of Cu2O Crystal Surface Energy

Abstract

In this study, we investigate the crystal structure, surface energy, and atomic arrangement of Cu2O. Understanding these properties is crucial for exploring the potential applications and understanding the behavior of this material. We employ the Wulff construction method and an artificial neural network (ANN) model to analyze the relative surface energies of different crystal facets and predict the surface energy of Cu2O. The ANN model exhibits excellent performance, demonstrating its effectiveness in predicting material properties and providing automated feature-learning and nonlinear-modeling capabilities. Moreover, we analyze the atomic arrangements on various crystal facets and observe the presence of oxygen atoms on the {100} facet, as well as exposed under-coordinated copper atoms on the {111} and {110} facets. High-index facets such as {211} exhibit a higher atomic step density and screw dislocation density. By precisely controlling the synthesis process, it is possible to manipulate the proportion of high-index facets. These findings highlight the significance of understanding the surface energy and atomic arrangement of Cu2O crystals for comprehending their properties and surface reactions. In summary, this study provides valuable insights into the crystal structure, surface energy, and atomic arrangement of Cu2O, offering inspiration for its properties and potential applications. The combination of the Wulff construction method and ANN modeling provides a comprehensive understanding of Cu2O crystals and their surface behavior, contributing to the field of materials science and laying the foundation for various future applications utilizing the unique properties of Cu2O.