Ternary metal halides based on Cu(I)and Ag(I)have attracted intensive attention in optoelectronic applications due to their excellent luminescent properties,low toxicity,and robust stability.While the self-trapped exc...Ternary metal halides based on Cu(I)and Ag(I)have attracted intensive attention in optoelectronic applications due to their excellent luminescent properties,low toxicity,and robust stability.While the self-trapped excitons(STEs)emission mechanisms of Cu(I)halides are well understood,the STEs in Ag(I)halides remain less thoroughly explored.This study explores the STE emission efficiency within the A_(2)AgX_(3)(A=Rb,Cs;X=Cl,Br,I)system by identifying three distinct STE states in each material and calculating their configuration coordinate diagrams.We find that the STE emission efficiency in this system is mainly determined by STE stability and influenced by self-trapping and quenching barriers.Moreover,we investigate the impact of structural compactness on emission efficiency and find that the excessive electron–phonon coupling in this system can be reduced by increasing the structural compactness.The atomic packing factor is identified as a low-cost and effective descriptor for predicting STE emission efficiency in both Cs_(2)AgX_(3) and Rb_(2)AgX_(3) systems.These findings can deepen our understanding of STE behavior in metal halide materials and offer valuable insights for the design of efficient STE luminescent materials.The datasets presented in this paper are openly available in Science Data Bank at https://doi.org/10.57760/sciencedb.12094.展开更多
Two-dimensional(2D) layered perovskites have emerged as potential alternates to traditional three-dimensional(3D)analogs to solve the stability issue of perovskite solar cells. In recent years, many efforts have been ...Two-dimensional(2D) layered perovskites have emerged as potential alternates to traditional three-dimensional(3D)analogs to solve the stability issue of perovskite solar cells. In recent years, many efforts have been spent on manipulating the interlayer organic spacing cation to improve the photovoltaic properties of Dion–Jacobson(DJ) perovskites. In this work, a serious of cycloalkane(CA) molecules were selected as the organic spacing cation in 2D DJ perovskites, which can widely manipulate the optoelectronic properties of the DJ perovskites. The underlying relationship between the CA interlayer molecules and the crystal structures, thermodynamic stabilities, and electronic properties of 58 DJ perovskites has been investigated by using automatic high-throughput workflow cooperated with density-functional(DFT) calculations.We found that these CA-based DJ perovskites are all thermodynamic stable. The sizes of the cycloalkane molecules can influence the degree of inorganic framework distortion and further tune the bandgaps with a wide range of 0.9–2.1 eV.These findings indicate the cycloalkane molecules are suitable as spacing cation in 2D DJ perovskites and provide a useful guidance in designing novel 2D DJ perovskites for optoelectronic applications.展开更多
Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design.It exploits the intelligent power of machine learning methods in massive materials data from experiments ...Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design.It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials,functionality,and principles,etc.Developing specialized facilities to generate,collect,manage,learn,and mine large-scale materials data is crucial to materials informatics.We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package(JAMIP),which is an open-source Python framework to meet the research requirements of computational materials informatics.It is integrated by materials production factory,high-throughput first-principles calculations engine,automatic tasks submission and monitoring progress,data extraction,management and storage system,and artificial intelligence machine learning based data mining functions.We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials.We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case.By obeying the principles of automation,extensibility,reliability,and intelligence,the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62125402 and 62321166653).
文摘Ternary metal halides based on Cu(I)and Ag(I)have attracted intensive attention in optoelectronic applications due to their excellent luminescent properties,low toxicity,and robust stability.While the self-trapped excitons(STEs)emission mechanisms of Cu(I)halides are well understood,the STEs in Ag(I)halides remain less thoroughly explored.This study explores the STE emission efficiency within the A_(2)AgX_(3)(A=Rb,Cs;X=Cl,Br,I)system by identifying three distinct STE states in each material and calculating their configuration coordinate diagrams.We find that the STE emission efficiency in this system is mainly determined by STE stability and influenced by self-trapping and quenching barriers.Moreover,we investigate the impact of structural compactness on emission efficiency and find that the excessive electron–phonon coupling in this system can be reduced by increasing the structural compactness.The atomic packing factor is identified as a low-cost and effective descriptor for predicting STE emission efficiency in both Cs_(2)AgX_(3) and Rb_(2)AgX_(3) systems.These findings can deepen our understanding of STE behavior in metal halide materials and offer valuable insights for the design of efficient STE luminescent materials.The datasets presented in this paper are openly available in Science Data Bank at https://doi.org/10.57760/sciencedb.12094.
基金supported by the National Natural Science Foundation of China (Grant No. 62004080)the Postdoctoral Innovative Talents Supporting Program (Grant No. BX20190143)the China Postdoctoral Science Foundation (Grant No. 2020M670834)。
文摘Two-dimensional(2D) layered perovskites have emerged as potential alternates to traditional three-dimensional(3D)analogs to solve the stability issue of perovskite solar cells. In recent years, many efforts have been spent on manipulating the interlayer organic spacing cation to improve the photovoltaic properties of Dion–Jacobson(DJ) perovskites. In this work, a serious of cycloalkane(CA) molecules were selected as the organic spacing cation in 2D DJ perovskites, which can widely manipulate the optoelectronic properties of the DJ perovskites. The underlying relationship between the CA interlayer molecules and the crystal structures, thermodynamic stabilities, and electronic properties of 58 DJ perovskites has been investigated by using automatic high-throughput workflow cooperated with density-functional(DFT) calculations.We found that these CA-based DJ perovskites are all thermodynamic stable. The sizes of the cycloalkane molecules can influence the degree of inorganic framework distortion and further tune the bandgaps with a wide range of 0.9–2.1 eV.These findings indicate the cycloalkane molecules are suitable as spacing cation in 2D DJ perovskites and provide a useful guidance in designing novel 2D DJ perovskites for optoelectronic applications.
基金supported by the National Natural Science Foundation of China(61722403,92061113,and 12004131)the Interdisciplinary Research Grant for Ph Ds of Jilin University(101832020DJX043)。
文摘Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design.It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials,functionality,and principles,etc.Developing specialized facilities to generate,collect,manage,learn,and mine large-scale materials data is crucial to materials informatics.We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package(JAMIP),which is an open-source Python framework to meet the research requirements of computational materials informatics.It is integrated by materials production factory,high-throughput first-principles calculations engine,automatic tasks submission and monitoring progress,data extraction,management and storage system,and artificial intelligence machine learning based data mining functions.We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials.We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case.By obeying the principles of automation,extensibility,reliability,and intelligence,the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.
