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 discovery of intrinsic magnetic topological order in $\rm MnBi_2Te_4$ has invigorated the search for materials with coexisting magnetic and topological phases. These multi-order quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute over 27,000 unique magnetic orderings for over 3,000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective excha…
Materials design based on density functional theory (DFT) calculations is an emergent field of great potential to accelerate the development and employment of novel materials. Magnetic materials play an essential role in green energy applications as they provide efficient ways of harvesting, converting, and utilizing energy. In this review, after a brief introduction to the major functionalities of magnetic materials, we demonstrated the fundamental properties which can be tackled via high-throughput DFT calculations, with a particular focus on the current challenges and feasible solutions. Su…
Due to their chemical and structural diversity, nanoporous materials can be used in a wide variety of applications, including fluid separation, gas storage, heterogeneous catalysis, drug delivery, etc. Given the large and rapidly increasing number of known nanoporous materials, and the even bigger number of hypothetical structures, computational screening is an efficient method to find the current best-performing materials and to guide the design of future materials. This review highlights the potential of high-throughput computational screenings in various applications. The achievements and t…
A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards…
Superconducting materials find applications in a rapidly growing number of technological areas, and searching for novel superconductors continues to be a major scientific task. However, the steady increase in the complexity of candidate materials presents a big challenge to the researchers in the field. In particular, conventional experimental methods are not well suited to efficiently search for candidates in compositional space exponentially growing with the number of elements; neither do they permit quick extraction of reliable multidimensional phase diagrams delineating the physical parame…
We perform high-throughput first-principles computations to search the high Curie temperature ($T_{\rm C}$) two-dimensional ferromagnetic (2DFM) materials. We identify 79 2DFM materials and calculate their $T_{\rm C}$, in which Co$_2$F$_2$ has the highest $T_{\rm C}$=541K, well above the room temperature. The 79 2DFM materials are classified into different structural prototypes according to their structural similarity. We perform sure independence screening and sparsifying operator (SISSO) analysis to explore the relation between $T_{\rm C}$ and the material structures. The results suggest tha…
Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a novel two-dimensional descriptor by applying an artificial intelligence (AI) based approach for fast and reliable identification of the topological characters of a drastically expanded …
Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality candidate materials are known. In this work, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from 40000 three-dimensional materials in the JARVIS-DFT database (https://jarvis.nist.gov/jarvisdft). First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candid…
Medium-Throughput Evaluation of Quantum Geometry-Driven Topological Transports in Altermagnets
Institute of Materials Science, Technical University of Darmstadt（达姆施塔特工业大学材料研究所）；Physics Department and CSMB, Humboldt-Universität zu Berlin（柏林洪堡大学物理系和CSMB）；School of Materials Science and Engineering, Jilin University（吉林大学材料科学与工程学院）；Suzhou Laboratory（苏州实验室）
The Automatic-Flow ( AFLOW ) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of solid state materials depend on a large number of parameters which must be understood by researchers, and must be reported by originators to ensure reproducibility and enable collaborative database expansion. We therefore describe standard parameter values for k-point grid density, basis set plane wave kinetic energy cut-off, exchange-correlation functionals, pseudopotentials, DFT+U parameters, and convergence criteria…
Geometric Approach to the High-Throughput Identification of Honeycomb Materials
Stewart Blusson Quantum Matter Institute, University of British Columbia（不列颠哥伦比亚大学斯特瓦特·布拉斯量子物质研究所）；Department of Physics & Astronomy, University of British Columbia（不列颠哥伦比亚大学物理与天文系）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）
Predicting material properties of disordered systems remains a long-standing and formidable challenge in rational materials design. To address this issue, we introduce an automated software framework capable of modeling partial occupation within disordered materials using a high-throughput (HT) first principles approach. At the heart of the approach is the construction of supercells containing a virtually equivalent stoichiometry to the disordered material. All unique supercell permutations are enumerated and material properties of each are determined via HT electronic structure calculations. …
This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stab…
We use a combination of symmetry analysis and high-throughput density functional theory calculations to search for new ferroelectric materials. We use two search strategies to identify candidate materials. In the first strategy, we start with non-polar materials and look for unrecognized energy-lowering polar distortions. In the second strategy, we consider polar materials and look for related higher symmetry structures. In both cases, if we find new structures with the correct symmetries that are also close in energy to experimentally known structures, then the material is likely to be switch…
The discovery and optimization of phase-change and shape memory alloys remain a tedious and expensive process. Here a simple computational method is proposed to determine the ideal phase-change material for a given alloy composed of three elements. Using first-principles calculations, within a high-throughput framework, the ideal composition of a phase-change material between any two assumed phases can be determined. This ideal composition minimizes the interface strain during the structural transformation. Then one can target this ideal composition experimentally to produce compounds with low…
We performed ab-initio driven density functional theory-based high throughput computations to search for materials with low thermal conductivity and high thermal transport anisotropy. We shortlisted a pool of 429 stable ternary semiconductors from the Materials Project and obtained phonon thermal conductivity by solving the Boltzmann transport equation on 225 materials. We found the lowest thermal conductivity of 0.16 W/m-K in SbRbK 2 and 40 materials with a thermal conductivity lower than 1 W/m-K at 300 K. For anisotropic thermal transport, we have identified six materials with anisotropy lar…
The current bulk materials discovery cycle has several inefficiencies from initial computational predictions through fabrication and analyses. Materials are generally evaluated in a singular fashion, relying largely on human-driven compositional choices and analysis of the volumes of generated data, thus also slowing validation of computational models. To overcome these limitations, we developed a high-throughput rapid experimental alloy development (HT-READ) methodology that comprises an integrated, closed-loop material screening process inspired by broad chemical assays and modern innovation…
Recently, Sten Haastrup, Mikkel Strange, Mohnish Pandey, Thorsten Deilmann, Per S Schmidt, Nicki F Hinsche, Morten N Gjerding, Daniele Torelli, Peter M Larsen, Anders C Riis-Jensen, Jakob Gath, Karsten W Jacobsen, Jens Jrgen Mortensen, Thomas Olsen and Kristian S Thygesen [2D Mater. 5 (2018) 042002] introduced the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than 30 different crystal structures. Unfortunately, the work conta…
The development of new high dielectric materials is essential for advancement in modern electronics. Oxides are generally regarded as the most promising class of high dielectric materials for industrial applications as they possess both high dielectric constants and large band gaps. Most previous researches on high dielectrics were limited to already known materials. In this study, we conducted an extensive search for high dielectrics over a set of ternary oxides by combining crystal structure prediction and density functional perturbation theory calculations. From this search, we adopted mult…
Finding cost-effective and efficient photocatalytic materials able to catalyse the water splitting reaction under visible light is one of the greatest challenges in current environmental material science. Despite that many photocatalysts are already known in the context of green hydrogen production, strategies to systematically and rationally modify their optoelectronic properties to achieve desired photocatalytic performance are yet to be established. Piezoelectric materials react to mechanical stimuli by adjusting their band gaps and band alignments, thus offering a possible route to precise…
The spin Hall effect (SHE) is an important spintronics phenomenon, which allows transforming a charge current into a spin current and vice versa without the use of magnetic materials or magnetic fields. To gain new insight into the physics of the SHE and to identify materials with a substantial spin Hall conductivities (SHC), we performed high-precision, high-throughput ab initio electronic structure calculations of the intrinsic SHC for over 20,000 non-magnetic crystals. The calculations reveal a strong and unexpected relation of the magnitude of the SHC with the crystalline symmetry, which w…
In this paper, we present the results of a large-scale, high-throughput computational search for electrides among all known inorganic materials. Analyzing a database of density functional theory results on more than 60,000 compounds, we identify 69 new electride candidates. We report on all these candidates and discuss the structural and chemical factors leading to electride formation. Among these candidates, our work identifies the first partially-filled 3d transition metal containing electrides Ba3CrN3 and Sr3CrN3; an unexpected finding that contravenes conventional chemistry.
The quasi-harmonic Debye approximation has been implemented within the AFLOW and Materials Project frameworks for high-throughput computational science (Automatic Gibbs Library, AGL), in order to calculate thermal properties such as the Debye temperature and the thermal conductivity of materials. We demonstrate that the AGL method, which is significantly cheaper computationally compared to the fully ab initio approach, can reliably predict the ordinal ranking of the thermal conductivity for several different classes of semiconductor materials. We also find that for the set of 182 materials inv…
The High-Throughput Experimental Materials Database (HTEM-DB) is the endpoint repository for inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This unique data asset is enabled by the Research Data Infrastructure (RDI) - a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data-tool workflow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of these data tools wi…
Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations. Unfortunately, many of the traditional methods fail when the global crystal symmetry is broken, even when the distortion is only a slight perturbation (e.g. Jahn-Teller like distortions). Here we introduce a flexible and generalizable parametric relaxation scheme, and implement it in the all-electron code FHI-aims. This approach utilizes parametric constraints to maintain symmetry at any level. After demonstrating the method's ability to relax metastable s…
The theory of symmetry indicators has enabled database searches for topological materials in normal conducting phases, which has led to several encyclopedic topological material databases. To date, such a database for topological superconductors is yet to be achieved because of the lack of information about pairing symmetries of realistic materials. In this work, sidestepping this issue, we tackle an alternative problem: the predictions of topological and nodal superconductivity in materials for each single-valued representation of point groups. Based on recently developed symmetry indicators …
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 Grüneisen parameter (γ), a descriptor of the lattice anharmonici…
A crucial problem in achieving innovative high-throughput materials growth with machine learning and automation techniques, such as Bayesian optimization (BO) and robotic experimentation, has been a lack of an appropriate way to handle missing data due to experimental failures. Here, we propose a new BO algorithm that complements the missing data in the optimization of materials growth parameters. The proposed method provides a flexible optimization algorithm capable of searching a wide multi-dimensional parameter space. We demonstrate the effectiveness of the method with simulated data as wel…
By high-throughput calculations based on the density functional theory, we construct a structure map for AB$_2$ type monolayers of 3844 compounds which are all the combinations of 62 elements selected from the periodic table. The structure map and its web version (www.openmx-square.org/2d-ab2/) provide comprehensive structural trends of the 3844 compounds in two dimensional (2D) structures, and predict correctly structures of most of existing 2D compounds such as transition metal dichalcogenides and MXenes having 1T or 1H type structures. We also summarize all the families of 1T/1H type AB$_2$…
Important phenomena such as magnetostriction, magnetocaloric, and magnetoelectric effects arise from, or could be enhanced by, the coupling of magnetic and structural degrees of freedom. The coupling of spin and lattice also influence transport and structural properties in magnetic materials in particular around phase transitions. In this paper we propose a method for screening materials for a strong magneto-structural coupling by assessing the effect of the local magnetic configuration on the atomic forces using density functional theory (DFT). We have employed the disordered local moment app…
Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively.…
We present a novel methodology to identify topologically non-trivial materials based on band inversion induced by spin-orbit coupling (SOC) effect. Specifically, we compare the density functional theory (DFT) based wavefunctions with and without spin-orbit coupling and compute the spin-orbit-spillage as a measure of band-inversion. Due to its ease of calculation, without any need for symmetry analysis or dense k-point interpolation, the spillage is an excellent tool for identifying topologically non-trivial materials. Out of 30000 materials available in the JARVIS-DFT database, we applied this…
Searching ferromagnetic semiconductor materials with electrically controllable spin polarization is a long-term challenge for spintronics. Bipolar magnetic semiconductors (BMS), with valence and conduction band edges fully spin-polarized in different spin directions, show great promise in this aspect because the carrier's spin polarization direction can be easily tuned by voltage gate. Here, we propose a standard high-throughput computational screening scheme for searching BMS materials. The application of this scheme to the Materials Project database gives 11 intrinsic BMS materials (1 experi…
2D materials find promising applications in next-generation devices, however, large-scale, low-defect, and reproducible synthesis of 2D materials remains a challenging task. To assist in the selection of suitable substrates for the synthesis of as-yet hypothetical 2D materials, we have developed an open-source high-throughput workflow package, $Hetero2d$, that searches for low-lattice mismatched substrate surfaces for any 2D material and determines the stability of these 2D-substrate heterostructures using density functional theory (DFT) simulations. $Hetero2d$ automates the generation of 2D-s…
Soft-phonon modes of an undistorted phase encode a material's preference for symmetry lowering. However, the evidence is sparse for the relationship between an unstable phonon wavevector's reciprocal and the number of formula units in the stable distorted phase. This "1/q*-criterion" holds great potential for the first-principles design of materials, especially in low-dimension. We validate the approach on the Q1D materials space containing 1199 ring-metal units and identify candidates that are stable in undistorted (1 unit), Peierls (2 units), charge density wave (3-5 units), or long wave (>5…
The diffusion of large databases collecting different kind of material properties from high-throughput density functional theory calculations has opened new paths in the study of materials science thanks to data mining and machine learning techniques. Phonon calculations have already been employed successfully to predict materials properties and interpret experimental data, e.g. phase stability, ferroelectricity and Raman spectra, so their availability for a large set of materials will further increase the analytical and predictive power at hand. Moving to a larger scale with density functiona…
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 structura…
In this paper, we incorporate processing into the data driven property modeling of Li-based spinel battery materials. When considering spinel compounds of the form LiMe2O4 with Me as a metal or metals, there are 125,000 possible combinations assuming a maximum of three metallic elements. In this work, we focus on capacity for our predicted property, and increase the number of possible combinations to two million by incorporating processing into the modeling. Due to the non-linear relationship between processing and property, as well as ensuring proper sensitivity, a new approach which tracks t…
We perform a computational screening for two-dimensional magnetic materials based on experimental bulk compounds present in the Inorganic Crystal Structure Database and Crystallography Open Database. A recently proposed geometric descriptor is used to extract materials that are exfoliable into two-dimensional derivatives and we find 85 ferromagnetic and 61 anti-ferromagnetic materials for which we obtain magnetic exchange and anisotropy parameters using density functional theory. For the easy-axis ferromagnetic insulators we calculate the Curie temperature based on classical Monte Carlo simula…
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 comp…
High throughput screening, crystal structure prediction, and carrier mobility calculations of organic molecular semiconductors as hole transport layer materials in perovskite solar cells
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 co…
