The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic(PV)and light-emitting diode(LED)applications lags far behind their 3D counterparts.Here,we propose a computational strategy for di...The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic(PV)and light-emitting diode(LED)applications lags far behind their 3D counterparts.Here,we propose a computational strategy for discovering novel perovskites with as few computing resources as possible.A tandem optimization algorithm consisting of an elitism-reinforced nondominated sorting genetic algorithm(NSGA-II)and a multiobjective Bayesian optimization(MOBO)algorithm was used for density functional theory(DFT)calculations.The DFT-calculated band gap and effective mass were taken as objective functions to be optimized,and the constituent molecules and elements of a Ruddlesden–Popper(RP)structure(n=2)were taken as decision variables.Fourteen previously unknown RP perovskite candidates for PV and LED applications were discovered as a result of the NSGA-II/MOBO algorithm.Thereafter,more accurate DFT calculations based on the HSE06 exchange correlation functional and ab initio molecular dynamics(AIMD)were conducted for the discovered 2D perovskites to ensure their validity.展开更多
We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimen...We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.展开更多
基金This research was supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT,and Future Planning(2021R1A2C1009144),(2021R1A2C1011642)and(2021M3A7C2089778).
文摘The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic(PV)and light-emitting diode(LED)applications lags far behind their 3D counterparts.Here,we propose a computational strategy for discovering novel perovskites with as few computing resources as possible.A tandem optimization algorithm consisting of an elitism-reinforced nondominated sorting genetic algorithm(NSGA-II)and a multiobjective Bayesian optimization(MOBO)algorithm was used for density functional theory(DFT)calculations.The DFT-calculated band gap and effective mass were taken as objective functions to be optimized,and the constituent molecules and elements of a Ruddlesden–Popper(RP)structure(n=2)were taken as decision variables.Fourteen previously unknown RP perovskite candidates for PV and LED applications were discovered as a result of the NSGA-II/MOBO algorithm.Thereafter,more accurate DFT calculations based on the HSE06 exchange correlation functional and ab initio molecular dynamics(AIMD)were conducted for the discovered 2D perovskites to ensure their validity.
基金supported by the Alchemist Project(20012196)funded by MOTIE,Koreapartly by the National Research Foundation of Korea(NRF)(2021R1A2C1009144 and RS-2024-00446825).
文摘We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.
