Multi‐metallic halides of group IA and IB metals are emerged as a new class of color tunable emitters. While chalcogenides and perovskites are extensively studied, these families of materials are little explored. In comparison, herein, lead and cadmium free bimetallic Cs‐Ag‐X (X = Cl, Br, I) halides are reported where the larger ion Ag+ helped in incorporating all the halide ions which in turn tune their emission color in spanning from 397 nm (violet) to 820 nm (near infrared) as a function of their composition. The synthesis method adopted here is the solvent free ball milling of respective halides of Cs and Ag and took the record shortest time and in bulk scale. From decay lifetimes, emissions from these bimetallic halides are found as a result of fast recombination of self‐trapped excitons, which exhibited not only reasonably high quantum yield in the range of 17–68% but also excellent stability to air and moisture under ambient conditions. These also show wide Stokes shift with relatively longer decay lifetimes ranging above the exciton and below the surface trap or dopant induced emissions of inorganic semiconductors, indicating a new class of materials having unique identity of their optical behaviors.

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Organic–inorganic hybrid semiconductors, of which organometal halide perovskites are representative examples, have drawn significant research interest as promising candidates for next-generation optoelectronic applications. This interest is mainly ascribed to the emergent optoelectronic properties of the hybrid semiconductors that are distinct from those of their purely inorganic and organic counterparts as well as different material fabrication strategies and the other material (e.g., mechanical) properties that combine the advantages of both. Herein, we present a high-throughput first-principles material screening study of the hybrid heterostructured semiconductors (HHSs) that differ entirely from organometal halide perovskite hybrid ion-substituting semiconductors. HHSs crystallize as superlattice structures composed of inorganic tetrahedrally coordinated semiconductor sublayers and organic sublayers made of bidentate chain-like molecules. By changing the composition (e.g., IV, III–V, II–VI, I–III–VI2 semiconductor) and polymorph (e.g., wurtzite and zinc-blende) of the inorganic components, the type of organic molecules (e.g., ethylenediamine, ethylene glycol, and ethanedithiol), and the thickness of the composing layers across 234 candidate HHSs, we investigated their thermodynamic, electronic structure, and optoelectronic properties. Thermodynamic stability analysis indicates the existence of 96 stable HHSs beyond the ZnTe/ZnSe-based ones synthesized experimentally. The electronic structure and optoelectronic properties of HHSs can be modulated over a wide range by manipulating their structural variants. A machine learning approach was further applied to the high-throughput calculated data to identify the critical descriptors determining thermodynamic stability and electronic band gap. Our results indicate promising prospects and provide valuable guidance for the rational design of organic–inorganic hybrid heterostructured semiconductors for potential optoelectronic applications.

With the rapid development of artificial intelligence and machine learning (ML) methods, materials science is rapidly entering the era of data-driven materials informatics. ML models serve as the most crucial component, closely bridging material structure and properties. There is a considerable difference in the prediction performance of different ML methods for material systems. Herein, we evaluated models of three categories (linear, kernel, and nonlinear method), with twelve ML algorithms commonly used in the materials field. In addition, halide perovskite was chosen as an example to evaluate the fitting performance of different models. We constructed a total dataset of 540 halide perovskites and 72 features, with formation energy and bandgap as target properties. We found that different categories of ML models show similar trends for different target properties. Among them, the difference between the models is enormous for the formation energy, with the coefficient of determination (R2) range: 0.69~0.953, while the fitting performance between the models whose R2 range: 0.941~0.997 is closer for bandgap. The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap. It shows that the nonlinear-ensemble model, constructed by combining multiple weak learners, effectively describes the nonlinear relationship between material features and target property. In addition, the eXtreme gradient boosting decision tree model performs the most superior results among all the models and the searching of two new descriptors that are crucial for formation energy and bandgap. Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.

Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for material science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAE) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structures data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating materials discovery process in the datadriven mode.

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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.