摘要
The vast compositional and configurational spaces of multi-elementmetal halide perovskites(MHPs)result in significant challenges when designing MHPs with promising stability and optoelectronic properties.In this paper,we propose a framework for the design of B-site-alloyed ABX_(3) MHPs by combining density functional theory(DFT)and machine learning(ML).We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids(PBEsol)on 3,159 B-sitealloyed perovskite structures using a compositional step of 1/4.
基金
supported by the National Research Foundation(NRF)grant funded by the Korean government(MSIT)(RS-2023-00283597)
the National Supercomputing Center with supercomputing resources and technical support(KSC-2019-CRE-0128).
