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Register a new userTernary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction
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We report the computational investigation of a series of ternary X$_4$Y$_2$Z and X$_5$Y$_2$Z$_2$ compounds with X={Mg, Ca, Sr, Ba}, Y={P, As, Sb, Bi}, and Z={S, Se, Te}. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on $textit{ab initio}$ calculations, we predict that many of these compounds are thermodynamically stable. In particular, 21 of the X$_4$Y$_2$Z compounds crystallize in a tetragonal structure with $textit{I-42d}$ symmetry, and exhibit band gaps in the range of 0.3 and 1.8 eV, well suited for various energy applications. We show that several candidate compounds (in particular X$_4$Y$_2$Te and X$_4$Sb$_2$Se) exhibit good photo absorption in the visible range, while others (e.g., Ba$_4$Sb$_2$Se) show excellent thermoelectric performance due to a high power factor and extremely low lattice thermal conductivities.
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Inorganic nitrides with wurtzite crystal structures are well-known semiconductors used in optoelectronic devices. In contrast, rocksalt-based nitrides are known for their metallic and refractory properties. Breaking this dichotomy, here we report on ternary nitride semiconductors with rocksalt crystal structures, remarkable optoelectronic properties, and the general chemical formula Mg$_{x}$TM$_{1-x}$N (TM=Ti, Zr, Hf, Nb). These compounds form over a broad metal composition range and our experiments show that Mg-rich compositions are nondegenerate semiconductors with visible-range optical absorption onsets (1.8-2.1 eV). Lattice parameters are compatible with growth on a variety of substrates, and epitaxially grown MgZrN$_{2}$ exhibits remarkable electron mobilities approaching 100 cm$^{2}$V$^{-1}$s$^{-1}$. Ab initio calculations reveal that these compounds have disorder-tunable optical properties, large dielectric constants and low carrier effective masses that are insensitive to disorder. Overall, these experimental and theoretical results highlight Mg$_{G-3}$TMN$_{G-2}$ rocksalts as a new class of semiconductor materials with promising properties for optoelectronic applications.
Geometric information such as the space groups and crystal systems plays an important role in the properties of crystal materials. Prediction of crystal system and space group thus has wide applications in crystal material property estimation and structure prediction. Previous works on experimental X-ray diffraction (XRD) and density functional theory (DFT) based structure determination methods achieved outstanding performance, but they are not applicable for large-scale screening of materials compositions. There are also machine learning models using Magpie descriptors for composition based material space group determination, but their prediction accuracy only ranges between 0.638 and 0.907 in different kinds of crystals. Herein, we report an improved machine learning model for predicting the crystal system and space group of materials using only the formula information. Benchmark study on a dataset downloaded from Materials Project Database shows that our random forest models based on our new descriptor set, achieve significant performance improvements compared with previous work with accuracy scores ranging between 0.712 and 0.961 in terms of space group classification. Our model also shows large performance improvement for crystal system prediction. Trained models and source code are freely available at url{https://github.com/Yuxinya/SG_predict}
Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations. Here we report an evolutionary algorithm powered search which uses machine-learned surrogate models trained on high-throughput hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN). The strategy enables efficient search over the materials space of ~10$^8$ ternaries and 10$^{11}$ quaternaries$^{7}$ for candidates with target properties. It provides interpretable design rules, such as our finding that the difference in the electronegativity between the halide and B-site cation being a strong predictor of ternary structural stability. As an example, when we seek UV emission, DARWIN predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materials family, based on its electronegativity difference. We synthesized and found these materials to be stable, direct bandgap UV emitters. The approach also allows knowledge distillation for use by humans.
Bandgap engineering by substituting C with B and N atoms in graphene has been shown to be a promising way to improve semiconducting properties of graphene. Such hybridized monolayers consisting of hexagonal BN phases in graphene (h-BNC) have been recently synthesized and char- acterized. In this paper, we present an ab initio density functional theory (DFT)-based study of h-BN domain size effect on band gap of mono-layer h-BNC heterostructures. The atomic structures, electronic band structures, density of states and electron localization functions of five h-BNC config- urations are examined as h-BN concentration ranged from 0 to 100%. We report that the band gap energy of h-BNC can be continuously and quadratically tuned as a function of h-BN concentration.
We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic cations, 10 metals (Pb, Sn, Bi, Cd, Cu, Fe, Ge, Mn, Pd, Sb) and 3 halogens (I, Br, Cl) known so far and will be regularly updated. The database contains a geometrical and crystal chemical analysis of the structures, which are useful to reveal quantitative structure-property relationships for this class of compounds. We show that the penetration depth of spacer organic cation into the inorganic layer and M-X-M bond angles increase in the number of inorganic layers (n). The machine learning model is developed and trained on the database, for the prediction of a band gap with accuracy within 0.1 eV. Another machine learning model is trained for the prediction of atomic partial charges with accuracy within 0.01 e. We show that the predicted values of band gaps decrease with an increase of the n and with an increase of M-X-M angles for single-layered perovskites. In general, the proposed database and machine learning models are shown to be useful tools for the rational design of new 2D hybrid perovskite materials.
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