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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.
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.