Density Functional Theory – Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden‐Popper Phases. Issue 19 (5th July 2018)

• Record Type: Journal Article
• Authors: Allam, Omar; Holmes, Colin; Greenberg, Zev; Kim, Ki Chul; Jang, Seung Soon
• Is Part Of: Chemphyschem. Volume 19:Issue 19(2018)
• Page Start: 2559
• Page End: 2565
• Publication Date: 2018-07-05
• Subjects: Perovskite -- Ruddlesden-Popper phase -- Density Functional Theory -- Machine Learning -- High-Throughput Screening; Chemistry, Physical and theoretical -- Periodicals; 541.05
• DOI: 10.1002/cphc.201800382
• Languages: English
• ISSNs: 1439-4235

Abstract

Abstract: In this study, we have developed a protocol for exploring the vast chemical space of possible perovskites and screening promising candidates. Furthermore, we examined the factors that affect the band gap energies of perovskites. The Goldschmidt tolerance factor and octahedral factor, which range from 0.98 to 1 and from 0.45 to 0.7, respectively, are used to filter only highly cubic perovskites that are stable at room temperature. After removing rare or radioactively unstable elements, quantum mechanical density functional theory calculations are performed on the remaining perovskites to assess whether their electronic properties such as band structure are suitable for solar cell applications. Similar calculations are performed on the Ruddlesden‐Popper phase. Furthermore, machine learning was utilized to assess the significance of input parameters affecting the band gap of the perovskites. Abstract : Quantum mechanical density functional theory modeling and machine learning have been applied to develop an artificial neural network that can accurately predict the band gap as a function of input parameters such as the number of layers, ionic radii, and charge states. This approach could be a useful tool for developing new solar‐cell materials.