One-dimensional materials have gained much attention in the last decades: from carbon nanotubes to ultrathin nanowires, to few-atom atomic chains, these can all display unique electronic properties and great potential for next-generation applications. Exfoliable bulk materials could naturally provide a source for one-dimensional wires with well defined structure and electronics. Here, we explore a database of one-dimensional materials that could be exfoliated from experimentally known three-dimensional Van-der-Waals compounds, searching metallic wires that are resilient to Peierls distortions and could act as vias or interconnects for future downscaled electronic devices. As the one-dimensional nature makes these wires particularly susceptible to dynamical instabilities, we carefully characterise vibrational properties to identify stable phases and characterize electronic and dynamical properties. Our search identifies several novel and stable wires; notably, we identify what could be the thinnest possible exfoliable metallic wire, CuC₂, coming a step closer to the ultimate limit in materials downscaling.

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.

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.

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.

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods. Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.

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).

In this paper, we present a workflow that is designed to work without manual intervention to efficiently predict, by using molecular simulations, the thermodynamic data that is needed to design a carbon capture process. We developed a procedure that does not rely on fitting of the adsorption isotherms. From molecular simulations, we can obtain accurate data for both, the pure component isotherms as well as the mixture isotherms. This allowed us to make a detailed comparison of the different methods to predict the mixture isotherms. All approaches rely on an accurate description of the pure component isotherms and a model to predict the mixture isotherms. As we are interested in low CO₂ concentrations, it is essential that these models correctly predict the low pressure limit, i.e., give a correct description of the Henry regime. Among the equations that describe this limit correctly, the dual-site Langmuir (DSL) model is often used for the pure components and the extended DSL (EDSL) for the mixtures. An alternative approach, which avoids describing the pure component isotherms with a model, is to numerically integrate the pure component isotherms in the context of IAST. In this work we compare these two methods. In addition, we show that the way these data are fitted for DSL can significantly impact the ranking of materials, in particular for capture processes with low concentration of CO₂ in the feed stream.

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 propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to solid-state materials is a nascent field with substantial fragmentation largely driven by the great variety of datasets used to develop machine learning models. This fragmentation makes comparing the performance and generalizability of different methods difficult, thereby hindering overall research progress in the field. Building on top of open-source datasets, including large-scale datasets like the OpenCatalyst Project, OQMD, NOMAD, the Carolina Materials Database, and Materials Project, the MatSci ML benchmark provides a diverse set of materials systems and properties data for model training and evaluation, including simulated energies, atomic forces, material bandgaps, as well as classification data for crystal symmetries via space groups. The diversity of properties in MatSci ML makes the implementation and evaluation of multi-task learning algorithms for solid-state materials possible, while the diversity of datasets facilitates the development of new, more generalized algorithms and methods across multiple datasets. In the multi-dataset learning setting, MatSci ML enables researchers to combine observations from multiple datasets to perform joint prediction of common properties, such as energy and forces. Using MatSci ML, we evaluate the performance of different graph neural networks and equivariant point cloud networks on several benchmark tasks spanning single task, multitask, and multi-data learning scenarios. Our open-source code is available at https://github.com/IntelLabs/matsciml.

Please consider citing the following paper:

@misc{carbon2020data,
 doi = {10.24435/MATERIALSCLOUD:2020.0026/V1},
 url = {https://archive.materialscloud.org/record/2020.0026/v1},
 author = {Pickard, Chris J.},
 keywords = {DFT, ab initio random structure searching, carbon},
 language = {en},
 title = {AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa},
 publisher = {Materials Cloud},
 year = {2020},
 copyright = {info:eu-repo/semantics/openAccess}
}

Osmium complexes with osmium in different oxidation states (II, III, IV, VI) have been reported to exhibit antiproliferative activity in cancer cell lines. Herein, we demonstrate unexplored opportunities offered by ¹⁸⁷Os nuclear forward scattering (NFS) and nuclear inelastic scattering (NIS) of synchrotron radiation for characterization of hyperfine interactions and lattice dynamics in a benchmark Os(VI) complex, K₂[OsO₂(OH)₄]. The isomer shift (𝛿 = 3.3(1) mm/s) relative to [Os^IVCl₆]^2– and quadrupole splitting (𝚫E_Q = 12.0(2) mm/s) were determined with NFS. The Lamb-Mössbauer factor (0.80(4)) is estimated, the density of phonon states (DOS) is extracted, and a thermodynamics characterization was carried out using the NIS data combined with first principles calculations. Overall, this study provides evidence that ¹⁸⁷Os nuclear resonance scattering is a reliable technique for the investigation of hyperfine interactions and Os specific vibrations in osmium(VI) species, and is thus applicable for such measurements in osmium complexes of other oxidation states, including those with anticancer activity such as Os(III) and Os(IV).

Customs records of are available for NEW MATERIAL CLOUD UNION TECHNOLOGY CO.,LTD. Learn about its Importer, supply capabilities and the countries to which it supplies goods

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.

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.

Cif files of relaxed "derived" copper surfaces from Cu2O, Cu2S, Cu3N, and Cu3P using EMT and published in J Gauthier, JH Stenlid, F Abild-Pedersen, M Head-Gordon, AT Bell, ACS Energy Letters, "The role of roughening to enhance selectivity to C2+ products during CO2 electroreduction on copper" (2021). These structures were used to evaluate the effect of roughening on catalytic selectivity of copper in electrochemcial CO2 reduction into valuable products such as fuels and commodity chemicals.

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

The global Materials Testing Software market is experiencing robust growth, driven by increasing demand for enhanced product quality and safety across diverse industries. The market is projected to reach a significant value, estimated at $2.5 billion in 2025, and is expected to maintain a healthy Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the expanding application of materials testing in sectors like automotive, aerospace, and construction for improved design and performance, and stringent regulatory compliance requirements necessitating advanced testing methodologies. The on-premises segment currently holds a larger market share due to established infrastructure and data security concerns, however, cloud-based solutions are rapidly gaining traction owing to their flexibility and accessibility. The automotive industry remains a dominant application segment, driven by the increasing complexity of vehicle designs and the need for rigorous testing to ensure safety and durability. Technological advancements, such as the integration of Artificial Intelligence (AI) and Machine Learning (ML) in materials testing software, are further propelling market expansion. These advancements automate data analysis, improve accuracy, and reduce testing time. However, the market faces certain restraints, including high initial investment costs for sophisticated software and the need for skilled personnel to operate and interpret the results. Despite these challenges, the long-term outlook for the Materials Testing Software market remains positive, with continuous innovation and expanding applications across diverse industries promising sustained growth in the coming years. Leading companies like AMETEK, Siemens, and Instron are actively contributing to this growth through continuous product development and strategic partnerships. Geographic expansion, particularly in rapidly developing economies within Asia Pacific and other emerging regions, also presents substantial opportunities for market growth.

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

This dataset deposits the supplemental materials for the manuscript "Cloud sync in response to wave-like large-scale forcings".

math_note.pdf A hand-written math derivation note for equations in the appendices.

movie_wL_006_T24hours.avi A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=24 hours experiment (the reference CM1 simulation).

movie_wL_006_T18hours.avi A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=18 hours experiment.

movie_wL_006_T12hours.avi A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=12 hours experiment.

movie_wL_000.avi A movie of near-surface (z=25m) water vapor mixing ratio, without large-scale wave-like forcing.

input_sounding The initial sounding for all CM1 simulations.

namelist.input The namelist file for launching all CM1 simulations.

postprocessing_CM1.zip The postprocessing code of the CM1 simulations, including intermediate output files (.mat) in data postprocessing.

microscopic_model.zip The MATLAB code for the microscopic model.

cm1.F The CM1 script where the large-scale vertical velocity is programmed. You can copy it directly to your CM1/src/ path.

Feel free to send an email to Dr. Hao Fu (haofu736@gmail.com) if you have any questions!

In this work we demonstrate the on surface synthesis of nonacene and heptacene and we discuss their open shell character comparing experimental evidence to theoretical predictions. The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images discussed.