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.

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.

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.

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.

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.

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.

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.

20 minute lightning talk presentation given by Aliaksandr Yakutovich, from École Polyechnique Fédérale de Lausanne, at the Better Science through Better Data 2018 event. The video recording and scribe are included.

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.

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.

We present a database of topological materials predicted from high-throughput first-principles calculations. The database contains electronic band structures and topological indices of 13628 materials calculated on experimental crystal structures taken from the Inorganic Crystal Structure Database (ICSD) and the Crystallography Open Database (COD). The calculations have been performed on non-magnetic phases taking into account the spin-orbit interactions using the Quantum ESPRESSO package. The Fu-Kane method and the Wannier charge center method implemented in the Z2pack code have been utilized to calculate the Z2 topological numbers of centrosymmetric and non-centrosymmetric materials, respectively. Over 4000 topologically non-trivial materials have been identified.

We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particularly for compounds characterized by both localized and hybridized states in the Hubbard manifold. The numerical values of all Hubbard parameters are readily obtained from linear-response calculations. The relevance of this more refined approach is showcased by its application to bulk solids pyrite (FeS₂) and pyrolusite (β-MnO₂), as well as to six Fe(II) molecular complexes. Our findings indicate that a careful definition of Hubbard manifolds is indispensable for extending the applicability of DFT+U beyond its current boundaries. The present orbital-resolved scheme aims to provide a computationally undemanding yet accurate tool for electronic structure calculations of charge-transfer insulators, transition-metal (TM) complexes and other compounds displaying significant orbital hybridization. This dataset contains all Quantum ESPRESSO input and output files as well as all pseudopotentials that were used to generate the results of this study. Moreover, an ``EXAMPLES'' folder provides guidance on how to apply the LR-cDFT approach to evaluate orbital-resolved DFT+U parameters in practise.

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 entry provides the snapshots of liquid water generated with ab initio molecular dynamics using rVV10 density functional at room temperature. Nuclear quantum effects are taken into account through path-integral molecular dynamics simulations.

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step.

In this set of supplemental materials, we have included the neural network potential for bulk water, including its training set in two different formats. We have also included the input files for running free energy calculations.

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional formulation. These functionals address two fundamental issues with density functional theory (DFT). First, while Kohn-Sham eigenvalues can loosely mirror experimental quasiparticle energies, they are not meant to reproduce excitation energies and there is formally no connection between the two (except for the HOMO for the exact functional). Second, (semi-)local DFT deviates from the expected piecewise linear behavior of the energy as a function of the total number of electrons. This can make eigenvalues an even poorer proxy for quasiparticle energies and, together with the absence of the exchange-correlation derivative discontinuity, contributes to DFT's underestimation of band gaps. By enforcing a generalized piecewise linearity condition to the entire electronic manifold, Koopmans functionals yield molecular orbital energies and solids-state band structures with comparable accuracy to many-body perturbation theory but at greatly reduced computational cost and preserving a functional formulation. This paper introduces "koopmans", an open-source package that contains all of the code and workflows needed to perform Koopmans functional calculations without requiring expert knowledge. The theory and algorithms behind Koopmans functionals are summarized, and it is shown how one can easily use the koopmans package to obtain reliable spectral properties of molecules and materials.

This archive contains files that accompany the article of the same name.

Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. To advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.

A user has submitted two similar records.

This contains the data used in the paper: "Intra-hour cloud index forecasting with data assimilation." It also includes supplementary material referenced in the above paper.

The CI_CMV_data.tar file contains data.nc and data_opt_flow.nc files that are in netCDF4 format. The data.nc files contain cloud index fields derived from GOES-15 geostationary satellite images and wind fields from a Weather Research and Forecasting (WRF) model run. The WRF_run_attributes.txt file contains the parameters of the WRF model that generated the wind fields contained in the data.nc files. The data_opt_flow.nc files contain dense optical flow cloud motion data that were derived using the method described in [1]. The data.nc and data_opt_flow.nc files are in a directory of the form: /yyyy/mm/dd/ corresponding to the year, month and day of the data.

The six videos are made from images from the GOES-15 geostationary satellite in the GOES-WEST position. The images are centered around Tucson, AZ. The domain of the images are from a latitude of approximately 27.22° N to 37.22° N and from a longitude of approximately 115.97° W to 105.97° W. All satellite images include a rectangle with dotted edges forming a region over Tucson, AZ that is 40 km from west to east and 56 km from south to north. Some satellite images contain a rectangle with solid edges. This rectangle surrounds a computational domain used in cloud index forecasting. Three of the six videos (sat_images_2014_04, sat_images_2014_05, and sat_images_2014_06) contain the images for April, May and June of 2014. The other three videos (sat_images_2014_04_15, sat_images_2014_04_26, and sat_images_2014_05_29) are for 4/15/2014, 4/26/2014, and 5/29/2014. There are also 500 mb height maps for these three days from NCEP.

[1] Sun, D., Roth, S., Black, M.J., 2010. Secrets of optical flow estimation and their principles, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2432–2439. doi:10.1109/CVPR.2010.5539939.