SciGlass Plus Database

Glass development is driven by scientific insights into their material genes as well as engineering practices in response to industry needs. Incubating domain knowledge into well-formatted datasets and databases, and making them open and visible can accelerate the process, facilitating automated material discovery and design in the data-driven era. In this work, we implement a machine-learning framework to collect information from published research articles. The enriched product, SciGlass+, transcends the publicly available glass database SciGlass by including both scientific data and research metadata.