Summary
Oxide glasses are one of the most important material families owing to their unique features, such as transparency, tunable properties, and formability. Emerging solutions to major global challenges related to energy, health, and electronics require new scientific breakthroughs in glass chemistry, mechanics, and processing. The realization of these goals is severely restricted by the main drawback of glass, namely high brittleness. Furthermore, new glass compositions are today developed through time-consuming trial-and-error experimentation due to their inherent non-equilibrium nature and disordered structure.
A major task is therefore to initiate a paradigm shift within the field of glass science and technology, going from empirical to model-based approaches for the design of new glass compositions and microstructures with improved fracture resistance. This requires the development of computational approaches, from ab initio calculations to artificial intelligence, to integrate structural descriptors and glass chemistry with advanced processing and mechanical properties into holistic tools.
NewGLASS challenges the current glass design strategies in order to create such tools. For this purpose, an interdisciplinary approach is proposed, in which structural descriptors at the short- and medium-range length scales are first identified and quantified based on emergent statistical mechanics and persistent homology techniques. Guided by these results, high-throughput simulations at various length scales are combined with machine learning algorithms to design novel glass compositions, tailored deformation mechanisms, and 3D-printed microstructures to achieve superior fracture resistance. By having experiments and modelling complement and advance each other reciprocally, NewGLASS will find order in disorder and provide the scientific breakthroughs for the accelerated design of glasses with outstanding mechanical performance, thus opening up for many new applications.
A major task is therefore to initiate a paradigm shift within the field of glass science and technology, going from empirical to model-based approaches for the design of new glass compositions and microstructures with improved fracture resistance. This requires the development of computational approaches, from ab initio calculations to artificial intelligence, to integrate structural descriptors and glass chemistry with advanced processing and mechanical properties into holistic tools.
NewGLASS challenges the current glass design strategies in order to create such tools. For this purpose, an interdisciplinary approach is proposed, in which structural descriptors at the short- and medium-range length scales are first identified and quantified based on emergent statistical mechanics and persistent homology techniques. Guided by these results, high-throughput simulations at various length scales are combined with machine learning algorithms to design novel glass compositions, tailored deformation mechanisms, and 3D-printed microstructures to achieve superior fracture resistance. By having experiments and modelling complement and advance each other reciprocally, NewGLASS will find order in disorder and provide the scientific breakthroughs for the accelerated design of glasses with outstanding mechanical performance, thus opening up for many new applications.
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| Web resources: | https://cordis.europa.eu/project/id/101044664 |
| Start date: | 01-09-2022 |
| End date: | 31-08-2027 |
| Total budget - Public funding: | 1 996 935,00 Euro - 1 996 935,00 Euro |
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Original description
Oxide glasses are one of the most important material families owing to their unique features, such as transparency, tunable properties, and formability. Emerging solutions to major global challenges related to energy, health, and electronics require new scientific breakthroughs in glass chemistry, mechanics, and processing. The realization of these goals is severely restricted by the main drawback of glass, namely high brittleness. Furthermore, new glass compositions are today developed through time-consuming trial-and-error experimentation due to their inherent non-equilibrium nature and disordered structure.A major task is therefore to initiate a paradigm shift within the field of glass science and technology, going from empirical to model-based approaches for the design of new glass compositions and microstructures with improved fracture resistance. This requires the development of computational approaches, from ab initio calculations to artificial intelligence, to integrate structural descriptors and glass chemistry with advanced processing and mechanical properties into holistic tools.
NewGLASS challenges the current glass design strategies in order to create such tools. For this purpose, an interdisciplinary approach is proposed, in which structural descriptors at the short- and medium-range length scales are first identified and quantified based on emergent statistical mechanics and persistent homology techniques. Guided by these results, high-throughput simulations at various length scales are combined with machine learning algorithms to design novel glass compositions, tailored deformation mechanisms, and 3D-printed microstructures to achieve superior fracture resistance. By having experiments and modelling complement and advance each other reciprocally, NewGLASS will find order in disorder and provide the scientific breakthroughs for the accelerated design of glasses with outstanding mechanical performance, thus opening up for many new applications.
Status
SIGNEDCall topic
ERC-2021-COGUpdate Date
09-02-2023
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