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An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
Renowned institutions in the field of tribology combine their testing and analytical capabilities with experts in materials/process information management technology to provide the respective Services in Europe (i-TRIBOMAT). This requires a centralized materials information management system or the standardised capture, consolidation and harmonization of tribological information.
Renowned institutions in the field of tribology combine their testing and analytical capabilities with experts in materials/process information management technology to provide the respective services in Europe (i-TRIBOMAT). This requires a centralized materials information management system or the standardised capture, consolidation and harmonization of tribological information.
A research project of reconstructing historical gold mosaics deals with reconstructing weatherproof gold and silver mosaics according to historical models.
The gold mosaics examinded originate mainly from germany from 1880 to 1920 and are composed of three parts. They consist of a matching support glass, the gold or silver layer and a cover glass. By examining the morphology and layer thicknesses with the ESEM, and using chemical analysis with the EDX, the relationship of glass composition and damage patterns was established. The glass analysis provided quantitative composition data and made it possible to determine its chemical stability. It supplied the basis for calculating glass characteristics and was referred to when casting suitable historical mosaic glass. The analysis of the qualitative composition of the gold and silver layers also served as a model for reconstruction. After testing selected reconstructed gold mosaics in the climate chamber, the ESEM was used for capturing leaching and other process data and the glass composition was optimized accordingly. Over 100 predominantly historical gold mosaics were analyzed and the results entered into a database which can now be consulted for comparative investigations to determine both origin and date.