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 FAIRsharing record describes: The Materials Cloud Archive provides FAIR & long-term storage of research data from computational materials science, with particular focus on sharing the full provenance of calculations.

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.

Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional calculations of crystals with consistent calculation parameters from various sources. The new dataset allows for the creation of a high quality convex hull and a large scale transfer learning approach. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD2. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.

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.

An Atom Probe Microscope (APM) is used to characterise the chemistry and 3D structure of materials (such as metals, alloys, semiconductors, superconductors, ceramics) at the atomic scale. The Atom Probe Workbench (APW) contains the computational tools that are necessary to analyse data from an APM, and runs on Linux (CentOS version 6.4) Virtual Machines within the NeCTAR cloud. The Atom Probe Workbench was developed as one of four application drivers of the NeCTAR Characterisation Virtual Laboratory (CVL) project: Energy Materials - Atom Probe. The APW is a suite of individual atom probe analysis tools (both open and closed source) developed by Atom Probe researchers around the world. APW also contains a collection of customised open source programs such as Galaxy (workflow engine), MyTardis (data sharing, usage tracking, citation reporting), and other command-line scripts (usage reporting). These open source programs provide additional functionality to allow a researcher to properly track and monitor the use of intellectual property. The pre-existing atom probe software tools included in version 1.0.0 of the Atom Probe Workbench are listed below (software name, software version, whether executable and/or can be part of a workflow, description, main author of the software). Each tool has a required citation as part of the terms of use, which can be found through the citation reporting link in MyTardis and in the online help. 1. 3Depict 0.0.15 (executable only): A visualisation and analysis tool for reconstructed atom probe data (open source code). Main author: Daniel Haley. 2. CAW 0.2.4 (executable and workflow): A cluster analysis wizard for reconstructed or simulated atom probe data, that works with the NN tool. Main author: Leigh T. Stephenson. 3. Crystallography 0.0.3 (executable and workflow): Determines the rotation matrix and Euler angles between two matrices describing the orientation. Main author: Vicente Araullo-Peters. 4. eff_off 0.2.1 (executable and workflow): Calculates the projected cluster size histogram, if detector efficiency was taken into account. Main author: Leigh T. Stephenson. 5. FourierTransform 0.2.3 (executable and workflow): Calculates the Fourier Transform of a reconstructed or simulated atom probe data. Main author: Anna V. Ceguerra 6. LevelSet 0.0.2 (executable and workflow): Simulation of ion trajectories in 2D, using an image (open source code). Main author: Daniel Haley. 7. MRF 0.2.2 (executable and workflow). 3D Markov Field cluster analysis for reconstructed or simulated atom probe data. Main author: Anna V. Ceguerra. 8. NN 0.2.4 (executable and workflow): Nearest neighbour analysis tool for reconstructed or simulated atom probe data. Main author: Leigh T. Stephenson. 9. OBJexport 0.0.4 (executable only): Export OBJ files from reconstructed or simulated atom probe data (open source code). Main author: Peter J. Felfer. 10. posgen 0.0.1 (executable and workflow): Atom probe data simulator for command line using XML input files (open source code). Main author: Daniel Haley. 11. POSminus 0.2.3 (executable and workflow): Removes the atoms found in the second file from the set of atoms found in the first file, within reconstructed or simulated atom probe data. Main author: Anna V. Ceguerra. 12. rdf-kd 0.0.1 (executable and workflow): Radial distribution function calculation tool for reconstructed or simulated atom probe data (open source code). Main author: Daniel Haley. 13. SDM_1D_calculate 2.0.2 (executable and workflow): 1D spatial distribution map calculation tool for reconstructed or simulated atom probe data. Main author: Michael P. Moody. 14. SDM_1D_plot 0.0.4 (executable and workflow): Plotting tool for SDM_1D_calculate output. Main author: Andrew J. Breen. 15. SDM_2D_calculate 2.0.2 (executable and workflow): 2D spatial distribution map calculation tool for reconstructed or simulated atom probe data. Main author: Michael P. Moody. 16. SDM_2D_plot 0.0.4 (executable and workflow): Plotting tool for SDM_2D_calculate output. Main author: Andrew J. Breen. 17. XRNGeditor 0.0.2 (executable only): A visual XRNG file creation tool for reconstructed or simulated atom probe data (open source code). Main author: Peter J. Felfer. Acknowledgements The authors acknowledge the facilities and the scientific and technical assistance of the Australian Microscopy & Microanalysis Research Facility at the Australian Centre for Microscopy & Microanalysis at the University of Sydney, in particular Thomson Chow, Takanori Sato and Deirdre Molloy. We acknowledge the work of the students and interns who were involved in this stage of the deployment effort, including Prasad Cheema, Alex Belini, and Edward Phillips. We are also thankful of the work by the Information and Communications Technologies (ICT) department at the University of Sydney: Justin Chang, Alexandra Voronova, Chris Albone, and Neal Anderson. We acknowledge the work of the core CVL technical staff, namely Anitha Kannan, Jupiter Hu, Chris Hines, James Wettenhall and Paul McIntosh, as well as the MyTardis team comprising Steve Androulakis, and Grischa Meyer. We also acknowledge the technical work performed by Intersect Australia team including Carlos Aya, David Angot, Cameron Maxwell, Sean Lin, and Ibrahim Taoube. University of Sydney is proud to be in partnership with the National eResearch Collaboration Tools and Resources (NeCTAR) project, Monash University, University of Queensland, ANU, AMMRF, ANSTO, Australian Synchrotron and the National Imaging Facility, to create the Characterisation Virtual Laboratory. This project will benefit the Australian research community by integrating Australia’s research imaging facilities with its computational, data storage infrastructure, and providing access to a wide variety of tools for data processing, analyses and visualisation.

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

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/.

Weyl semimetals are crystals in which electron bands cross at isolated points in momentum space. Associated with each crossing point (or Weyl node) is an integer topological invariant known as the Berry monopole charge. The discovery of new classes of Weyl materials is driving the search for novel properties that derive directly from the Berry charge. The circular photogalvanic effect (CPGE), whereby circular polarized light generates a current whose direction depends on the helicity of the absorbed photons, is a striking example of a macroscopic property that emerges from Weyl topology. Recently, it was predicted that the rate of current generation associated with optical transitions near a Weyl node is proportional to its monopole charge. In Weyl semimetals that retain mirror symmetry the current is strongly suppressed by contributions from energy equivalent nodes of opposite charge. However, when all mirror symmetries are broken, as in chiral Weyl systems, nodes with opposite topological charge are no longer degenerate, opening a window of photon energies where the CPGE derived from the topological band structure can emerge. In this work we report the photon-energy dependence of the CPGE in the chiral Weyl semimetal RhSi. The spectrum reveals a helicity-sensitive response in a low-energy window that closes at 0.65 eV, in quantitative agreement with the bandstucture recently predicted from DFT calculations.

We have included the code of protocol verification for ProVerif (in .pv format) and Scyther (in .spdl format) in the “Supplementary Materials” (Supplementary-Materials.pdf) of the revised manuscript. Essentially, the .pdf file of the “Supplementary Materials” includes: A. The source code for the protocol verifier Scyther (in .spdl format); and B. The source code for the protocol verifier ProVerif (in .pv format).

mesh_0.ply : before draping of ply mesh_1.ply: after draping of ply mesh_2.ply: after debullking

The purpose of this work is to examine environmental effects of materials which are intended for use in in situ processing systems.

Established as part of the Energy Materials Network, under the U.S. Department of Energy's Clean Energy Manufacturing Initiative, the mission of the Lightweight Materials National Lab Consortium is to create an enduring national lab-based network, enabling industry to utilize the national labs' unique capabilities related to lightweight materials.

Cloud Microservices Latest Research Report. Complete Market Research, Market Analysis, CAGR, Trends, Major Players, Market Share, Market Size.

A catalog of all Materials Licensing Actions received for review. The catalog lists the name of the entity submitting the license application, their city and state, the license number, the docket number, the NRC Region that handled the review, and the typ

The materials program is a part of the overall experimental program of the IEA Pressurized Fluidized Bed Combustion Facility at Grimethorpe, South Yorkshire, UK. It comprises component monitoring, component failure analysis and inspection, as well as material tests and evaluation. This report describes the materials studies for the period of operation of the Facility with Tube Bank 'C2'; earlier reports cover the periods with 'A'and 'C'. An overall assessment of materials performance during the entire operational period has also been provided. 40 refs., 66 figs., 27 tabs.

Metal-organic frameworks (MOFs) have emerged as versatile materials for applications ranging from gas separation and storage, catalysis, and sensing. The attractive feature of MOFs is that by changing the ligand and/or metal, they can be chemically tuned to perform optimally for a given application. In most, if not all, of these applications one also needs a material that has a sufficient mechanical stability, but our understanding of how changes in the chemical structure influence mechanical stability is limited. In this work, we rationalize how the mechanical properties of MOFs are related to framework bonding topology and ligand structure. We illustrate that the functional groups on the organic ligands can either enhance the mechanical stability through formation of a secondary network of non-bonded interactions, or soften the material by destabilizing the bonded network of a MOF. In addition, we show that synergistic effect of the bonding network of the material and the secondary network is required to achieve optimal mechanical stability of a MOF. The developed molecular insights in this work can be used for systematic improvement of the mechanical stability of the materials by careful selection of the functional groups.

The Long Term Materials Test has been built, shaken down, and tested extensively during the last year, fiscal 1981. Startup and operating procedures have been established and the rig is now ready for long term testing. A fully instrumented and monitored Qualification Test has established that the PFB rig can provide 90% sulfur capture, acceptable dust load, and 1600/sup 0/F combustion products at the low velocity test section (without supplemental gas firing). The rig operates as designed.

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical description of these effects requires that the nuclei move on a temperature-dependent electronic free energy. We propose a method to obtain machine-learning predictions of this free energy at an arbitrary electron temperature using exclusively training data from ground-state calculations, avoiding the need to train temperature-dependent potentials, and benchmark it on metallic liquid hydrogen at the conditions of the core of gas giants and brown dwarfs. This Letter demonstrates the advantages of hybrid schemes that use physical consideration to combine machine-learning predictions, providing a blueprint for the development of similar approaches that extend the reach of atomistic modeling by removing the barrier between physics and data-driven methodologies.

This record contains the raw outputs of the DFT calculations done on the training set. These files are denoted by the "training set folder #*" description. The record also contains a minimal working example showing the ML workflow for training the data and how to run the MD simulations. We include the training data in the format of XYZ files for the structures and NumPy arrays for the DFT energies, forces and DOS. We also provide a Chemiscope visualisation file of the training set.