Descrizione del progetto
Un innovativo quadro computazionale a sostegno della progettazione razionale di vetri in ossido a elevate prestazioni
I vetri in ossido costituiscono un’ampia famiglia di vetri con applicazioni diffuse e in crescita, dal vetro per finestre e contenitori ai display a cristalli liquidi e alle fibre ottiche delle telecomunicazioni. Nonostante il loro utilizzo diffuso, la fragilità rappresenta ancora una sfida. Inoltre, la comprensione di base delle loro proprietà e dei loro comportamenti è piuttosto limitata, il che impedisce il ricorso a una progettazione razionale e conduce a metodi per tentativi inefficienti che richiedono tempo. Il progetto NewGLASS, finanziato dall’UE, svilupperà approcci computazionali che affrontino il comportamento di questi composti intrinsecamente disordinati e la loro natura di non equilibrio su tutte le scale, promuovendo future scoperte scientifiche nella progettazione di nuovi vetri con prestazioni senza precedenti.
Obiettivo
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.
Campo scientifico
- engineering and technologymaterials engineering
- natural sciencesphysical sciencesclassical mechanicsstatistical mechanics
- natural sciencescomputer and information sciencescomputational sciencemultiphysics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencessoftwaresoftware applicationssimulation software
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-AG - HORIZON Action Grant Budget-BasedIstituzione ospitante
9220 Aalborg
Danimarca