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Register a new userDiscovery of Hidden Classes of Layered Electrides by Extensive High-throughput Materials Screening
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Despite their extraordinary properties, electrides are still a relatively unexplored class of materials with only a few compounds grown experimentally. Especially for layered electrides, the current researches mainly focus on several isostructures of Ca2N with similar interlayer two-dimensional (2D) anionic electrons. An extensive screening for different layered electrides is still missing. Here, by screening materials with anionic electrons for the structures in Materials Project, we uncover 12 existing materials as new layered electrides. Remarkably, these layered electrides demonstrate completely different properties from Ca2N. For example, unusual fully spin-polarized zero-dimensional (0D) anionic electrons are shown in metal halides with MoS2-like structures; unique one-dimensional (1D) anionic electrons are confined within the tubes of the quasi-1D structures; a coexistence of magnetic and non-magnetic anionic electrons is found in ZrCl-like structures and a new ternary Ba2LiN with both 0D and 1D anionic electrons. These materials not only significantly increase the pool of experimentally synthesizable layered electrides but also are promising to be exfoliated into advanced 2D materials.
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The high-throughput (HT) computational method is a useful tool to screen high performance functional materials. In this work, using the deformation potential method under the single band model, we evaluate the carrier relaxation time and establish an electrical descriptor (c{hi}) characterized by the carrier effective masses based on the simple rigid band approximation. The descriptor (c{hi}) can be used to reasonably represent the maximum power factor without solving the electron Boltzmann transport equation. Additionally, the Gruneisen parameter ({gamma}), a descriptor of the lattice anharmonicity and lattice thermal conductivity, is efficiently evaluated using the elastic properties, omitting the costly phonon calculations. Applying two descriptors (c{hi} and {gamma}) to binary chalcogenides, we HT compute 243 semiconductors and screen 50 promising thermoelectric materials. For these theoretically determined compounds, we successfully predict some previously experimentally and theoretically investigated promising thermoelectric materials. Additionally, 9 p-type and 14 n-type previously unreported binary chalcogenides are also predicted as promising thermoelectric materials. Our work provides not only new thermoelectric candidates with perfect crystalline structure for the future investigations, but also reliable descriptors to HT screen high performance thermoelectric materials.
High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at our Carolina Materials Database http://www.carolinamatdb.org.
The discoveries of intrinsically magnetic topological materials, including semimetals with a large anomalous Hall effect and axion insulators, have directed fundamental research in solid-state materials. Topological quantum chemistry has enabled the understanding of and the search for paramagnetic topological materials. Using magnetic topological indices obtained from magnetic topological quantum chemistry (MTQC), here we perform a high-throughput search for magnetic topological materials based on first-principles calculations. We use as our starting point the Magnetic Materials Database on the Bilbao Crystallographic Server, which contains more than 549 magnetic compounds with magnetic structures deduced from neutron-scattering experiments, and identify 130 enforced semimetals (for which the band crossings are implied by symmetry eigenvalues), and topological insulators. For each compound, we perform complete electronic structure calculations, which include complete topological phase diagrams using different values of the Hubbard potential. Using a custom code to find the magnetic co-representations of all bands in all magnetic space groups, we generate data to be fed into the algorithm of MTQC to determine the topology of each magnetic material. Several of these materials display previously unknown topological phases, including symmetry-indicated magnetic semimetals, three-dimensional anomalous Hall insulators and higher-order magnetic semimetals. We analyse topological trends in the materials under varying interactions: 60 per cent of the 130 topological materials have topologies sensitive to interactions, and the others have stable topologies under varying interactions. We provide a materials database for future experimental studies and open-source code for diagnosing topologies of magnetic materials.
The recent observation of ferromagnetic order in two-dimensional (2D) materials has initiated a booming interest in the subject of 2D magnetism. In contrast to bulk materials, 2D materials can only exhibit magnetic order in the presence of magnetic anisotropy. In the present work we have used the Computational 2D Materials Database (C2DB) to search for new ferromagnetic 2D materials using the spinwave gap as a simple descriptor that accounts for the role of magnetic anisotropy. In addition to known compounds we find 12 novel insulating materials that exhibit magnetic order at finite temperatures. For these we evaluate the critical temperatures from classical Monte Carlo simulations of a Heisenberg model with exchange and anisotropy parameters obtained from first principles. Starting from 150 stable ferromagnetic 2D materials we find five candidates that are predicted to have critical temperatures exceeding that of CrI3. We also study the effect of Hubbard corrections in the framework of DFT+U and find that the value of U can have a crucial influence on the prediction of magnetic properties. Our work provides new insight into 2D magnetism and identifies a new set of promising monolayers for experimental investigation.
Great enthusiasm in single atom catalysts (SACs) for the N2 reduction reaction (NRR) has been aroused by the discovery of Metal (M)-Nx as a promising catalytic center. However,the performance of available SACs,including poor activity and selectivity,is far away from the industrial requirement because of the inappropriate adsorption behaviors of the catalytic centers. Through the first principles high throughput screening, we find that the rational construction of Fe-Fe dual atom centered site distributed on graphite carbon nitride (Fe2/gCN) compromises the ability to adsorb N2H and NH2, achieving the best NRR performance among 23 different transition metal (TM) centers. Our results show that Fe2/gCN can achieve a Faradic efficiency of 100% for NH3 production. Impressively, the limiting potential of Fe2/gCN is estimated as low as -0.13 V, which is hitherto the lowest value among the reported theoretical results. Multiple level descriptors (excess electrons on the adsorbed N2 and integrated crystal orbital Hamilton population) shed light on the origin of NRR activity from the view of energy, electronic structure, and basic characteristics. As a ubiquitous issue during electrocatalytic NRR, ammonia contamination originating from the substrate decomposition can be surmounted. Our predictions offer a new platform for electrocatalytic synthesis of NH3, contributing to further elucidate the structure-performance correlations.
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