Crystal structure prediction and property calculation of copper–oxygen compounds using innovative search software from first principles

Abstract

A Bayesian optimisation algorithm for deep learning crystal structure prediction software (CBD-GM) is used to predict the structures of Cu(I) and Cu(II) oxides of 2D and 3D materials. Two known 2D structures and two known 3D structures were anticipated, in addition to the prediction of 5 novel structures. All nine structures were optimised and analysed using density-functional theory (DFT). Firstly, DFT calculations using the PBE functional indicate that the structures should be thermodynamically and dynamically stable. Secondly, we calculated the elastic constants using the “stress–strain” method, and the predicted Young's modulus and Poisson's ratios of the materials suggest that they all should have excellent ductile mechanical properties. Calculations of the band structure of the materials performed using the Heyd–Scuseria–Ernzerhof (HSE) hybrid functional indicate that some of the materials should be semiconductors with useful bandgaps. The results therefore provide inspiration for the synthesis of new copper oxides for industrial applications.