The Materials Cloud three-dimensional database is a curated set of relaxed three-dimensional crystal structures based on raw CIF data taken from the external experimental databases MPDS, COD and ICSD. The raw CIF data have been imported, cleaned and parsed into a crystal structure; their ground-state has been computed using the SIRIUS-enabled pw.x code of the Quantum ESPRESSO distribution, and tight tolerance criteria for the calculations using the SSSP protocols. This entire procedure is encoded into an AiiDA workflow which automates the process while keeping full data provenance. Here, since the original source data of the ICSD and MPDS databases are copyrighted, only the provenance of the final SCF calculation on the relaxed structures can be made publicly available. The MC3D ID numbers come from a list of unique "parent" stoichiometric structures that has been created and curated from a collection of these experimental databases. Once a parent structure has been optimized using density-functional theory, it is made public and added to the online Discover section of the Materials Cloud (as mentioned, copyright might prevent publishing the original parent). Note that since not all structures have been calculated, some ID numbers are missing from the public version of the database. The full ID of each structure also contains as an appended modifier the functional that was used in the calculations. Since the ID number points to the same unique parent, mc3d-1234/pbe and mc3d-1234/pbesol have the same starting point, but have been then relaxed according to their respective functionals.

Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

Koopmans' spectral functionals aim to describe simultaneously ground state properties and charged excitations of atoms, molecules, nanostructures and periodic crystals. This is achieved augmenting standard density functionals with simple but physically motivated orbital-density-dependent corrections. These corrections act on a set of localized orbitals that, in periodic systems, resembles maximally localized Wannier function. At variance with a direct supercell implementation, we discuss here i) the complex but efficient formalism required for a periodic-boundary code using explicit Brillouin zone sampling, and ii) the calculation of the screened Koopmans' corrections with density-functional perturbation theory. In addition to delivering improved scaling with system size, the present development makes the calculation of band structures with Koopmans functionals straightforward. The implementation in the Quantum ESPRESSO distribution and the application to prototypical insulating and semiconducting systems are presented and discussed.

We introduce OSCAR, a repository of thousands of experimentally derived (OSCAR seed and CSD-extracted) and combinatorially enriched organocatalysts (OSCAR!(NHC) and OSCAR!(DHBD) for N-heterocyclic carbenes and hydrogen bond donors, respectively). The structures and corresponding stereoelectronic properties are publicly available and constitute the starting point to build generative and predictive models for organocatalyst performance.

The self-consistent evaluation of Hubbard parameters using linear-response theory is crucial for quantitatively predictive calculations based on Hubbard-corrected density-functional theory. Here, we extend a recently-introduced approach based on density-functional perturbation theory (DFPT) for the calculation of the on-site Hubbard U to also compute the inter-site Hubbard V. DFPT allows to reduce significantly computational costs, improve numerical accuracy, and fully automate the calculation of the Hubbard parameters by recasting the linear response of a localized perturbation into an array of monochromatic perturbations that can be calculated in the primitive cell. In addition, here we generalize the entire formalism from norm-conserving to ultrasoft and projector-augmented wave formulations, and to metallic ground states. After benchmarking DFPT against the conventional real-space Hubbard linear response in a supercell, we demonstrate the effectiveness of the present extended Hubbard formulation in determining the equilibrium crystal structure of LiₓMnPO₄ (x=0,1) and the subtle energetics of Li intercalation.

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

TCSP 2.0 templte database, it includes the Materials Project (MP) database, Materials Cloud database (both 2D and 3D), The Computational 2D Materials Database (C2DB), and Graph Networks for Materials Science database(GNoME).

Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid State Pseudopotential (SSSP) efficiency and precision libraries, tailored for high-throughput materials screening and high-precision materials modelling. As of today, the SSSP precision library is the most accurate open-source pseudopotential library available. This archive entry contains the database of calculations (phonons, cohesive energy, equation of state, band structure, pressure, etc.) together with the provenance of all data and calculations as stored by AiiDA.

*** UPDATE April 2023 - Version 1.3.0 *** The pseudopotentials of elements At, Fr, Ra are added from PSlibrary. The pseudopotential of actinides are added from dataset of https://www.uni-marburg.de/de/fb15/arbeitsgruppen/anorganische_chemie/ag-kraus/forschung/paw_datasets_for_the_actinoids

In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for an increased accuracy in the calculations. Here, we present the updated alexandria dataset of calculations for more than 415k solid-state materials obtained with two improved functionals: PBE for solids (that yields consistently better geometries than the PBE) and SCAN (probably the best all-around functional at the moment). Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for more reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBE for solids, and SCAN.

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

We present a database of energy and NMR chemical shifts DFT calculations of 4150 crystal organic solids. The structures contain only H/C/N/O/S atoms and were subject to all-atoms geometry optimisation. Calculations were carried out using Quantum Espresso and GIPAW.

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

'Magic'-angle twisted bilayer graphene has received a lot of interest due to its flat bands with potentially non-trivial topology that lead to intricate correlated phases. A spectrum with flat bands, however, does not require a twist between multiple sheets of van der Waals materials, but rather can be realized with the application of an appropriate periodic potential. Here, we propose the imposition of a tailored periodic potential onto a single graphene layer through local perturbations that could be created via lithography or adatom manipulation, which also results in an energy spectrum featuring flat bands. Our first-principle calculations for an appropriate decoration of graphene with adatoms indeed show the presence of flat bands in the spectrum. Furthermore, we reveal the topological nature of the flat bands through a symmetry-indicator analysis. This non-trivial topology manifests itself in corner-localized states with a filling anomaly as we show using a tight-binding model. Our proposal of a single decorated graphene sheet provides a new versatile route to study correlated phases in topologically non-trivial, flat band structures.

We elucidate the nature of the electron-phonon interaction in the archetypal hybrid perovskite CH₃NH₃PbI₃ using ab initio many-body calculations and an exactly solvable model. We demonstrate that electrons and holes near the band edges primarily interact with three distinct groups of longitudinal-optical vibrations, in order of importance: the stretching of the Pb-I bond, the bending of the Pb-I-Pb bonds, and the libration of the organic cations. These polar phonons induce ultrafast intraband carrier relaxation over timescales of 6–30 fs and yield polaron effective masses 28% heavier than the bare band masses. These findings allow us to rationalize previous experimental observations and provide a key to understanding carrier dynamics in halide perovskites.

Using high-throughput automation of ab-initio impurity-embedding simulations we created a database of 3d and 4d transition metal defects embedded into the prototypical topological insulators (TIs) Bi₂Te₃ and Bi₂Se₃. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of non-magnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian. We uncover chemical trends in the exchange coupling constants and discuss the impurities' potential with respect to magnetic order in the fields of quantum anomalous Hall insulators. In particular, we predict that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.

This record contained data and/or metadata that may have infringed upon the copyright of the publisher of the corresponding article. A new version of this record is available via the DOI 10.24435/materialscloud:2019.0084/v2

This record was removed upon request from the authors who had identified errors in it.

The book is divided into six major parts with the following headings- Materials considerations and performance data, materials testing and research results, properties of candidate materials, properties of experimental materials, and references.

Cloud-SPAN is a collaboration between the Department of Biology at the University of York and The Software Sustainability Institute funded by the UKRI innovation scholars award. It aims to train researchers to effectively generate and analyse a range of ‘omics data using Cloud computing resources. We have found that people taking the Genomics module can vary the amount of experience they have had in navigating file systems and using the command line. We have designed another module, Prenomics, to prepare those with less experience for Genomics. We have a Self-assessment Quiz to help you decide if you would benefit from Prenomics before the Genomics module. The Prenomics module assumes no prior experience and is designed for absolute beginners. The Prenomics and Genomics modules are based on the Data Carpentry’s Genomics Workshop. Genomics teaches data management and analysis for genomics research including: (1) best practices for organization of bioinformatics projects and data, (2) use of command-line utilities to connect to and use cloud computing and storage resources, (3) use of command-line tools for data preparation, (4) use of command-line tools to analyze sequence quality and perform and automate variant calling. The module is designed for a four half-day, tutor-led workshop, or for self study. The Genomics module comprises five GitHub repositories: The Overview repository for webpages at https://cloud-span.github.io/00genomics/ Session 1: Project Management for Cloud Genomics repository for webpages at https://cloud-span.github.io/01genomics/ Session 2: Data Preparation and Organisation repository for webpages at https://cloud-span.github.io/02genomics/ Session 3: Assessing Read quality then Trimming and Filtering Reads for webpages at https://cloud-span.github.io/03genomics/ Session 4: Finding Sequence Variants repository for webpages at https://cloud-span.github.io/04genomics/

Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.