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.

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.

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).

Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to performed machine-learning assisted high-throughput materials searches including 2500 binary and ternary prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and around 150000 compounds with a distance of less than 50 meV/atom from the hull. Here we include the DCGAT-1, DCGAT-2, and DCGAT-3 datasets used in this work.

Understanding how the vibrational and thermal properties of solids are influenced by atomistic structural disorder is of fundamental scientific interest, and paramount to designing materials for next-generation energy technologies. While several studies indicate that structural disorder strongly influences the thermal conductivity, the fundamental physics governing the disorder-conductivity relation remains elusive. Here we show that order-of-magnitude, disorder-induced variations of conductivity in network solids can be predicted from a bond-network entropy, an atomistic structural descriptor that quantifies heterogeneity in the topology of the atomic-bond network. We employ the Wigner formulation of thermal transport to demonstrate the existence of a relation between the bond-network entropy, and observables such as smoothness of the vibrational density of states (VDOS) and macroscopic conductivity. We also show that the smoothing of the VDOS encodes information about the thermal resistance induced by disorder, and can be directly related to phenomenological models for phonon-disorder scattering based on the semiclassical Peierls-Boltzmann equation. Our findings rationalize the conductivity variations of disordered carbon polymorphs ranging from nanoporous electrodes to defective graphite used as a moderator in nuclear reactors. This database contains the models of structures reported in the paper referenced below.

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.

A machine-learned interatomic potential for Ge-rich Ge_xTe alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase change memories which exploits Ge-enrichment of GeSbTe alloys to raise the crystallization temperature. The potential is generated by fitting a large database of energies and forces computed within Density Functional Theory with the neural network scheme implemented in the DeePMD-kit package. The potential is highly accurate and suitable to describe the structural and dynamical properties of the liquid, amorphous and crystalline phases of the wide range of compositions from pure Ge and stoichiometric GeTe to the Ge-rich Ge₂Te alloy. Large scale molecular dynamics simulations revealed a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess was observed to occur on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe was observed to occur before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess.

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.

Topological Weyl semimetals represent a novel class of quantum materials that exhibit remarkable properties arising from their unique electronic structure. In this work, we employ state-of-the-art ab initio methods to investigate the role of the electron-phonon interactions on the charge transport properties of TaAs. Our calculations of the temperature-dependent electrical conductivity with the iterative Boltzmann transport equation show excellent agreement with experimental measurements above 100 K. Extending the analysis to doped systems, we demonstrate that even small shifts in the Fermi level can lead to substantial changes in conductivity, driven by the complex topology of the Fermi surface. In particular, modifications in Fermi surface nesting emerge as a key factor influencing scattering processes and carrier lifetimes. These findings offer critical insights into the microscopic mechanisms that govern transport in TaAs and highlight the sensitivity of Weyl semimetals to doping and carrier dynamics. This record includes (i) the code and input files used for the calculations, and (ii) the data and plotting scripts used to generate the figures in the paper.

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.

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.

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.

The JuHemd (Jülich-Heusler-magnetic-database) is a collection of the magnetic phase transition types and transition temperatures (Tc) for experimentally documented Heusler and Heusler-like materials, as found by density functional calculations augmented by the Monte Carlo method, and as reported by experiment in the literature. The database contains results on 400 compounds, many of them with different setups of the chemical order/disorder, totaling 776 systems. The ground state electronic structure was obtained by density functional theory calculations with the JuKKR code. Two exchange-correlation functionals were employed: the local density approximation (LDA) and the generalized gradient approximation (GGA). The chemical disorder was treated within the coherent-potential approximation. For 306 materials (627 systems) with sizable magnetization, the Heisenberg exchange parameters were evaluated using the method of infinitesimal rotations and the Tc was determined by our in-house Monte Carlo program. The simulated data for all the compounds in the database are accompanied by experimental data published in the literature: the crystal structure, the lattice constant, the Tc and the character of the magnetic phase transition. The correlation between the experimental reports and the results for LDA and GGA functionals and the statistical analysis of these results will be published elsewhere.

The long-wavelength behavior of vibrational modes plays a central role in carrier transport, phonon-assisted optical properties, superconductivity, and thermomechanical and thermoelectric properties of materials. Here, we present general invariance and equilibrium conditions of the lattice potential; these allow to recover the quadratic dispersions of flexural phonons in low-dimensional materials, in agreement with the phenomenological model for long-wavelength bending modes. We prove that for any low-dimensional material, the bending modes can have a purely out-of-plane polarization in the vacuum direction and a quadratic dispersion in the long-wavelength limit. In addition, we propose an effective approach to treat the invariance conditions in crystals with non-vanishing Born effective charges where the long-range dipole-dipole interactions induce a contribution to the stress tensor. Our approach has been successfully applied to the phonon dispersions of 158 two-dimensional materials, opening new avenues for the studies of phonon-mediated properties of low-dimensional materials. The dataset uploaded here contains an AiiDA database for new phonon dispersions of all 245 two-dimensional materials produced in this work and essential data for reproducing the main results of this work. These data include the modified q2r and matdyn code of Quantum ESPRESSO distribution, pseudopotentials used in this work, optimized crystal structures, interatomic force constants and phonon dispersions.

A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed. Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features.

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