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.

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.

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.

Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first- principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high- thoughput (HT) computational materials design is an emergent field that promises to accelerate the reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method (SCDM) and we present the details of its implementation in an AiiDA workflow. We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space. We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations. We provide here an AiiDA export file with the full provenance of all simulations run in the project. Moreover, we provide a downloadable virtual machine that allows to reproduce the results of this paper and also to run new calculations for different materials, including all first-principles and atomistic simulations and the computational workflows.

The creation and maintenance of crystallographic data repositories is one of the greatest data-related achievements in chemistry. Platforms such as the Cambridge Structural Database host what is likely the most diverse collection of synthesizable molecules. If properly mined, they could be the basis for the large-scale exploration of new regions of the chemical space using quantum chemistry (QC). However, it is currently challenging to retrieve all the necessary information for QC based exclusively on the available structural data, especially for transition metal complexes. To solve this shortcoming, we present cell2mol, a software that interprets crystallographic data and retrieves the connectivity and total charge of molecules, including the oxidation state (OS) of metal atoms. We prove that cell2mol outperforms other popular methods at assigning the metal OS, while offering a much more comprehensive interpretation of the unit cell, and we make publicly available reliable QC-ready databases totaling 31k transition metal complexes and 13k ligands, encompassing incomparable chemical diversity.

This record contains the aforementioned database of crystallographic structures after interpretation using the cell2mol software. The database spans 8 different transition metals (Fe, Mn, Ru, Re, Cr, Co, Ni, Cu; named from 1 to 8) and contains over 31000 different transition metal complexes and 13000 unique ligands, but also contains the interpreted contents of the entire unit cells in terms of discrete chemical species with well-defined charges and connectivities. Details can be found in the README.txt file and an exemplary script is provided for usage. The cell2mol code can be obtained in https://github.com/lcmd-epfl/cell2mol.

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.

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.

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.

This record is maintained in the National Geologic Map Database (NGMDB). The NGMDB is a Congressionally mandated national archive of geoscience maps, reports, and stratigraphic information, developed according to standards defined by the cooperators, i.e., the USGS and the Association of American State Geologists (AASG). Included in this system is a comprehensive set of publication citations, stratigraphic nomenclature, downloadable content, unpublished source information, and guidance on standards development. The NGMDB contains information on more than 90,000 maps and related geoscience reports published from the early 1800s to the present day, by more than 630 agencies, universities, associations, and private companies. For more information, please see http://ngmdb.usgs.gov/.

The advent of the Internet of Things has increased the interest in automating mission-critical processes from domains such as smart cities. These applications' stringent Quality of Service (QoS) requirements motivate their deployment through the Cloud-IoT Continuum, which requires solving the NP-hard problem of placing the application's services onto the infrastructure's devices. Moreover, as the infrastructure and application change over time, the placement needs to continuously adapt to these changes to maintain an acceptable QoS. While continuous reasoning techniques have enabled the creation of tools for these scenarios, they can have some trouble finding a feasible adaptation for abrupt and sharp changes, requiring non-adaptive techniques in those cases. Furthermore, for scenarios with smoother changes, it would be desirable to have faster algorithms to perform this placement. To explore the trade-off of effectiveness and execution times of different methods while ensuring that an application placement is found, we propose Multi-Layered Continuous Reasoning (MLCR) as a framework to adapt application placements through multiple continuous reasoning-based methods. We also present an MLCR prototype based on three methods: Faustum, MigDADO, and ConDADO. An evaluation in a realistic use case shows that MLCR is faster than traditional methods for application placement and maintains an acceptable QoS.

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.

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Dipole polarizabilities, computed using linear response coupled cluster theory and density functional theory (using d-aug-cc-pVDZ basis set), for 7211 molecules from the QM7b dataset of small molecules and for 52 molecules from a showcase dataset.

In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive set of fixed-charge and variable-charge potentials tailored for alumina. We also train a machine learning interatomic potential, using the NequIP framework. Results highlight that the fixed-charge potential by Matsui provides an ideal blend of computational speed and alignment with ab initio findings for stoichiometric alumina. For non-stoichiometric variants, the variable charge potentials, especially ReaxFF, align remarkably well with DFT outcomes. The NequIP ML potential, while superior in some instances and adaptable, might not be the best fit for specific tasks.

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.

Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. For their uptake and release, non-covalent interactions between the framework and the drugs play a fundamental role. However, an in-depth understanding of how different functional groups affect these interactions is still lacking. Using a multilevel approach combining molecular docking and density functional theory, we show in the publication associated with this data how computational modeling can be exploited to gain information on the interplay between functionalization and drug delivery features. We find that functional groups significantly impact the strength of the host-guest interactions. Moreover, the interaction strength qualitatively correlates with drug release data from experimental literature, providing a link between framework structure and release properties. The results of our study facilitate the customization of non-covalent interactions between MOFs/COFs and drugs and give direction on how to rationally design nanocarriers for sustaining the drug release in the body over longer times. The data deposited on the Materials Cloud Archive contains the inputs and outputs of the molecular docking and DFT simulations.