Data-driven workflow for accelerated glass development
Project runtime: 01.02.2021 – 31.01.2024
Contact Person(s)
Publications
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Genome Mining in Glass Chemistry Using Linear Component Analysis of Ion Conductivity Data |
Zhiwen Pan, Jan Dellith, Lothar Wondraczek (2025)
| DOI: DOI: 10.1002/advs.202301435
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Ontology-Based Digital Infrastructure for Data-Driven Glass Development |
Ya-Fan Chen, Felix Arendt, Hansjörg Bornhöft, Andréa S. S. de Camargo, Joachim Deubener, Andreas Diegeler, Shravya Gogula, Altair T. Contreras Jaimes, Sebastian Kempf, Martin Kilo, René Limbach, Ralf Müller, Rick Niebergall, Zhiwen Pan, Frank Puppe, Stefan Reinsch, Gerhard Schottner, Simon Stier, Tina Waurischk, Lothar Wondraczek, Marek Sierka (2025)
| DOI: https://doi.org/10.1002/adem.202401560
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Enhancing glass property predictions through ab initio-derived descriptors |
Felix Arendt, René Limbach, Lothar Wondraczek, Marek Sierka (2025)
| DOI: DOI: 10.1111/jace.19904
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Optical Real-Time Castability Evaluation for High-Throughput Glass Melting |
Shravya Gogula, Hansjörg Bornhöft, Lothar Wondraczek, Marek Sierka, Andreas Diegeler, Ralf Müller, Joachim Deubener (2025)
| DOI: DOI: 10.52825/glass-europe.v2i.1359
Poster
2023-09-22_Vollversammlung_Poster_Anlage_GlasDigital
2023-09-22_Vollversammlung_Poster_Projektbeschreibung_GlasDigital
2023-09-22_Vollversammlung_Poster_Infrastruktur_GlasDigital
Vorträge
2023-09-22_Vollversammlung_GlasDigital
2021-06-10_BMBF_KickOff_GlasDigital
2022-03-17_Vollversammlung_GlasDigital
2024-06-21_GlasDigital_Abschlusstreffen
Fully automated intelligent expert system for high-throughput glass development
Within the joint project GlasDigital, digital tools for the development of novel glass materials are to be developed. Current processes for the production of glasses with improved properties are usually very cost- and energy-intensive due to the low degree of automation and are subject to long development cycles. Particular challenges arise from the wide variability of possible chemical compositions, the high process temperatures required for melting and the creation of suitable interfaces to artificially intelligent control devices. The use of robotic synthesis processes in combination with self-learning machines is intended to overcome these problems in the long term. The development of new types of glass - for example with higher mechanical strength - can then not only be accelerated considerably, but also carried out with much less effort.
