Huo Jinrong, Zhang Kai, Liu Pengfei, Wei Haocong, He Chaozheng
School of Sciences, Xi'an Technological University, Xi'an, Shaanxi 710021, China.
Institute of Environmental and Energy Catalysis, School of Materials Science and Chemical Engineering, Xi'an Technological University, Xi'an, Shaanxi 710021, China.
Phys Chem Chem Phys. 2024 Sep 18;26(36):24078-24089. doi: 10.1039/d4cp02501f.
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.
一种用于深度学习晶体结构预测软件（CBD - GM）的贝叶斯优化算法被用于预测二维和三维材料的Cu(I)和Cu(II)氧化物的结构。除了预测5种新结构外，还预期了两种已知的二维结构和两种已知的三维结构。使用密度泛函理论（DFT）对所有九种结构进行了优化和分析。首先，使用PBE泛函的DFT计算表明这些结构在热力学和动力学上应该是稳定的。其次，我们使用“应力 - 应变”方法计算了弹性常数，材料预测的杨氏模量和泊松比表明它们都应该具有优异的延性机械性能。使用Heyd - Scuseria - Ernzerhof（HSE）杂化泛函对材料的能带结构进行的计算表明，其中一些材料应该是具有有用带隙的半导体。因此，这些结果为工业应用中新型氧化铜的合成提供了灵感。
