A curated database of computationally relaxed three-dimensional crystal structures based on raw data from experimental crystallographic sources.

The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening as primary goal rather than robust force-field learning, are limited in their scope to a specific class of chemical compounds, and/or employ inconsistent DFT functionals and settings. Here we introduce MAD-1.5, a highly curated dataset designed explicitly for training broadly applicable atomistic models across the periodic table at high levels of theory. MAD-1.5 extends the MAD dataset with targeted enrichment strategies that improve the coverage of chemical space to 102 elements while keeping the total number of configurations compact. All structures are computed with a single, standardized all-electron DFT workflow using the r2SCAN meta-GGA functional and consistent convergence settings, ensuring uniformity across chemically heterogeneous systems. The dataset encompasses molecules, clusters, bulk crystals, surfaces, and low-dimensional structures, and its quality and consistency are further enhanced by outlier removal using uncertainty quantification. We demonstrate the high accuracy that can be achieved with the proposed dataset by training PET-MAD-1.5, a generally applicable r2SCAN interatomic potential that covers 102 elements in the periodic table and achieves exceptional levels of benchmark accuracy and stability in challenging simulation protocols.

The development of machine-learning models for atomic-scale simulations has benefitted tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train “universal” models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery, and therefore targeted primarily to sample stable, or at least plausible, structures and to make the most accurate predictions for each compound – e.g. adjusting the calculation details to the material at hand. Here we introduce a dataset designed specifically to train models that can provide reasonable predictions for arbitrary structures, and that therefore follows a different philosophy. Starting from relatively small sets of stable structures, the dataset is built to contain “massive atomic diversity” (MAD) by aggressively distorting these configurations, with near-complete disregard for the stability of the resulting configurations. The electronic structure details, on the other hand, are chosen to maximize consistency rather than to obtain the most accurate prediction for
a given structure, or to minimize computational effort. The MAD dataset we present here, despite containing fewer than 100k structures, has already been shown to enable training universal interatomic potentials that are competitive with models trained on traditional datasets with two to three orders of magnitude more structures. We describe in detail the philosophy and details of the construction of the MAD dataset. We also introduce a low-dimensional structural latent space that allows us to compare it with other popular datasets, and that can also be used as a general-purpose materials cartography tool.

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.

Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent ''universal'' models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable MLIP trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but highly-consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with state-of-the-art MLIPs for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.

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.

Summing all Feynman diagrams with quantitative accuracy is a holy grail in theoretical physics. In condensed matter, the lattice vibration (phonon) field couples with the electrons, leading to the formation of entangled electron-phonon (e-ph) states called polarons. In the intermediate- and strong-coupling regimes common to many conventional and quantum materials, a many-body treatment of polarons requires adding up a large number of e-ph diagrams. Diagrammatic Monte Carlo (DMC) is an efficient method for diagram summation and has been employed to study polarons within simplified e-ph models (Holstein, Frohlich, etc.). Here we show DMC calculations based on accurate first-principles e-ph interactions, enabling numerically exact results for ground-state and dynamical properties of polarons in real materials, including the polaron formation energy, effective mass, spectral weight, phonon cloud distribution, optical conductivity, and mobility. We demonstrate such DMC calculations in systems with polarons ranging from small (localized) to large (delocalized), including LiF, SrTiO3, and TiO2 rutile and anatase. This advance is enabled by our recently developed technique for compressing first-principles e-ph interaction matrices, together with a matrix-product formalism that mitigates the DMC sign problem from multiple electronic bands. Our work enables precise modeling of e-ph interactions and polarons in coupling regimes ranging from weak to strong, opening doors to studies of transport, linear response, and superconductivity in the strong e-ph coupling regime.

We present a sulfur K-edge X-ray absorption near-edge structure (XANES) database of 18 crystalline and 48 amorphous Lithium-Phosphorous-Sulfur (LPS) compounds. The database contains a total of 2681 XANES spectra of symmetrically inequivalent absorbing S sites. Structures were taken from Materials Cloud entry 2022.17 (archive.materialscloud.org/record/2022.17) and were originally generated by systematically removing Li, P and S atoms from known crystal structures using an evolutionary algorithm and an artificial neural network based interatomic potential. The details of this procedure can be found in Guo et al. (see references below). From this data set, low-energy structures were selected for spectral simulations. The excited electron and core hole method as implemented in VASP 6.2.1 was used to compute the XANES spectra for each symmetrically inequivalent Sulfur atom. The details of the VASP simulations can be found in the associated manuscript.

Acknowledgements: We acknowledge financial support by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Contract No. DE-SC0012704. These results used the computational resources of the Center for Functional Nanomaterials and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under the Contract No. DE-SC0012704. We also acknowledge computing resources from Columbia University's Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010.

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).

Magnet/superconductor hybrid systems have been put forward as a platform for realizing topological superconductivity. We investigated the heterostructure of ferromagnetic monolayer CrCl₃ and superconducting NbSe₂. Using low-temperature scanning tunneling microscopy, we observe topologically trivial Yu-Shiba-Rusinov (YSR) states localized at the edge of CrCl₃ islands. DFT simulations reveal that the Cr atoms at the edge have an enhanced d-orbital DOS close to the Fermi energy. This leads to an exchange coupling between these atoms and the substrate that rationalizes the edge-localization of the YSR states.

This dataset contains the first-principles calculations performed on a nanoribbon of CrCl₃ on NbSe₂ and the associated notebooks used to generate figures from this data.

Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: p_z tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.

Magnetic materials often exhibit complex energy landscapes with multiple local minima, each corresponding to a self-consistent electronic structure solution. Finding the global minimum is challenging, and heuristic methods are not always guaranteed to succeed. We apply an automated workflow to systematically explore the energy landscape of 194 magnetic monolayers from the Materials Cloud 2D crystals database and determine their ground-state magnetic order. Our approach enables effective control and sampling of orbital occupation matrices, allowing rapid identification of local minima. We reveal a diverse set of self-consistent collinear metastable states, further enriched by Hubbard-corrected energy functionals with U parameters computed from first principles using linear response theory. We categorize the monolayers by their magnetic ordering and highlight promising candidates for applications.

The cloud-based molecular modeling software market is booming, projected to reach $6.12 billion by 2033 with a 15% CAGR. Discover key trends, drivers, restraints, and leading companies shaping this dynamic sector. Explore applications in drug discovery, materials science, and more.

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.

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.

A major challenge in first-principles high-throughput materials simulations is automating the selection of parameters used by simulation codes in a way that robustly ensures numerical precision and computational efficiency. Here, we propose a rigorous methodology to assess the quality of self-consistent DFT calculations with respect to smearing and k-point sampling across a wide range of crystalline materials. To achieve this, we develop criteria to reliably control average errors in total energies, forces, and other properties as a function of the desired computational efficiency, while consistently suppressing uncontrollable k-point sampling errors. Our results provide automated protocols for selecting optimized parameters based on different precision and efficiency tradeoffs. This archive contains all data related to the material structures and calculation workflows developed in this work.

Discover the booming market for materials testing software! Learn about key trends, growth drivers, and leading companies shaping this $2.5B industry. Explore regional market shares and projections through 2033, focusing on cloud-based solutions and their impact. Get valuable insights into the future of materials testing.

Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs.
This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm. For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 extends the dataset to moieties from C12 to C18. This version (v3) extends the dataset with new configurations for moieties from C2 to C18. The dataset will be extended to characterize the Si(110) surface of the nanowire. For each system the most stable configuration will be identified, and the analysis of the electronic properties will be conducted.

Two-dimensional (2D) layered materials offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS2 and multi-layer graphene moiré assemblies.