Glass databases are a collection of glass compositions, glass properties, glass models, associated trademark names, patents etc. These data were collected from publications in scientific papers and patents, from personal communication with scientists and engineers, and other relevant sources.
History
Since the beginning of scientific glass research in the 19th century, thousands of glass property-composition datasets were published. The first attempt to summarize all those data systematically was the monograph "Glastechnische Tabellen".[1]World War II and the Cold War prevented similar efforts for many years afterwards.
In 1956, "Phase Diagrams for Ceramists" was published the first time, containing a collection of phase diagrams.[2] This database is known today as "Phase Equilibria Diagrams".[3]
in 1983, the "Handbook of Glass Data" was published,[4] followed by the creation of the Japanese database Interglad in 1991.[5] The "Handbook of Glass Data" was later digitalized and substantially expanded under the name SciGlass.[6] Currently, SciGlass contains properties of about 400,000 glass compositions, INTERGLAD about 380,000,[7] and "Phase Equilibria Diagrams" includes about 31,000 diagrams.
In 2023, the re-emergence of the SciGlass database as SciGlass Sage[9] offered "AI" assistance, a property predictor powered by random forest regression models, and a generator using predictive models in conjunction with genetic algorithms.
In 2024, SciGlass Next was created as an open-access web database utilizing the SciGlass data available on GitHub.[8] The database is hosted in the public domain of Friedrich Schiller University Jena.
Screenshot of Statistics of glasses in SciGlass Next database
The website provides comprehensive documentation, including step-by-step instructions and glossaries of properties and symbols used.
Most features are covered, including:
Glasses: 422,000+ glasses and melts. Sourced from 40,000+ literature sources, including 19,700+ patents.
Data Tables: Search data and export tables for post-processing.
Data Visualization: Interactive data visualization with scatter plots, histograms, ternary plots, and curve fitting.
Authentication: Secured Single Sign-On (SSO) authentication of users.
ML Predictions (Future): Python-backed ML predictions for glass properties.
Sidebar Quick Lookup: Categories of patent index, trademark index, author index, subject index, spectral index and glass formation.
Glass database contents
The following list of glass database contents is not complete, and it may not be up to date. For full features see the references section below. All databases contain citations to the original data sources and the chemical composition of the glasses or ceramics.
SciGlass: Viscosity, density, mechanical properties, optical properties (including optical spectra), thermal expansion and other thermal properties, electrical properties, chemical durability, liquidus temperatures, crystallization characteristics, ternary diagrams of glass formation, glass property calculation methods, patent and trademark index, subject index etc.
