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Register a new userHigh-throughput discovery of novel cubic crystal materials using deep generative neural networks
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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.
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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.
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.
We present a review of topological electronic materials discovery in crystalline solids from the prediction of the first 2D and 3D topological insulators (TIs) through the recently introduced methods that have facilitated large-scale searches for topological materials. We first briefly review the concepts of band theory and topology, as well as the experimental methods used to demonstrate nontrivial topology in solid-state materials. We then review the past 15 years of topological materials discovery, including the identification of the first nonmagnetic TIs, topological crystalline insulators (TCIs), and topological semimetals (TSMs). Most recently, through complete analyses of symmetry-allowed band structures - including the theory of Topological Quantum Chemistry (TQC) - researchers have determined crystal-symmetry-enhanced Wilson-loop and complete symmetry-based indicators for nonmagnetic topological phases, leading to the discovery of higher-order TCIs and TSMs. Lastly, we discuss the recent application of TQC and related methods to high-throughput materials discovery, which revealed that over half of all of the known stoichiometric, solid-state, nonmagnetic materials are topological at the Fermi level, over 85% of the known stoichiometric materials host energetically isolated topological bands, and that just under $2/3$ of the energetically isolated bands in known materials carry the stable topology of a TI or TCI. We conclude by discussing future venues for the identification and manipulation of solid-state topological phases, including charge-density-wave compounds, magnetic materials, and 2D few-layer devices.
Crystal structure determines properties of materials. With the crystal structure of a chemical substance, many physical and chemical properties can be predicted by first-principles calculations or machine learning models. Since it is relatively easy to generate a hypothetical chemically valid formula, crystal structure prediction becomes an important method for discovering new materials. In our previous work, we proposed a contact map-based crystal structure prediction method, which uses global optimization algorithms such as genetic algorithms to maximize the match between the contact map of the predicted structure and the contact map of the real crystal structure to search for the coordinates at the Wyckoff Positions(WP). However, when predicting the crystal structure with high symmetry, we found that the global optimization algorithm has difficulty to find an effective combination of WPs that satisfies the chemical formula, which is mainly caused by the inconsistency between the dimensionality of the contact map of the predicted crystal structure and the dimensionality of the contact map of the target crystal structure. This makes it challenging to predict the crystal structures of high-symmetry crystals. In order to solve this problem, here we propose to use PyXtal to generate and filter random crystal structures with given symmetry constraints based on the information such as chemical formulas and space groups. With contact map as the optimization goal, we use differential evolution algorithms to search for non-special coordinates at the Wyckoff positions to realize the structure prediction of high-symmetry crystal materials. Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry.
Within 4 different crystal structures, 2280 ternary intermetallic configurations have been investigated via high-throughput density functional theory calculations in order to discover new semiconducting materials. The screening is restricted to intermetallics with the equimolar composition TMX, where T is a transition metal from the Ti, V, Cr columns, Sr, Ba, Y and La, M an element from the first line of transition metals and X a sp elements (Al, P, Si, Sn and Sb), i.e. to a list of 24 possible elements. Since the calculations are done combinatorically, every possible ternary composition is considered, even those not reported in the literature. All these TMX configurations are investigated in the 4 most reported structure-types: TiNiSi, MgAgAs, BeZrSi and ZrNiAl. With an excellent agreement between calculations and literature for the reported stable phases, we identify 472 possible stable compounds among which 21 are predicted as non-metallic. Among these 21 compositions, 4 could be considered as new semiconductors.
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