Schematic illustration for the seed project: Machine learning for predicting the mechanical properties of high performance oxynitride glasses

Seed project: Machine learning for predicting the mechanical properties of high performance oxynitride glasses

This project aims to develop a machine learning (ML) model to predict the mechanical properties of high-performance oxynitride glasses, which are challenging to analyze with traditional methods. The goal is to enhance material design for industries like displays, electronics and aerospace by leveraging ML for accurate and efficient property predictions.

Project information

Project manager
Sharafat Ali
Other project members
Shafiullah Soomro, Shahid Sattar, Hatem Algabroun, Karolina Milewska
Participating organizations
Linnaeus University
Financier
Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), Carl Trygger Foundation
Timetable
2nd May 2025 till 31 Dec 2025
Subject
Materials Science (with a focus on Glass Science), (Department of Built Environment and Energy Technology (BET), data analysis, machine learning and computer science, Department of Computer Science and Media Technology, Department of Mechanical Engineering, and Department of Physics and Electrical Engineering, Faculty of Technology).
Research Group
Smart Industry Group (SIG)
Centre of Excellence
Linnaeus University Centre for Data Intensive Sciences and Applications (DISA)
Linnaeus Knowledge Environment
Linnaeus Knowledge Environment: Advanced Materials

More about the project

This seed project focuses on using machine learning to predict the mechanical properties of oxynitride glasses—advanced materials that are stronger, tougher, and more heat-resistant than regular glass. By replacing some oxygen with nitrogen, these glasses are made more durable, making them ideal for demanding applications like displays, electronics, aerospace, and automotive industries. However, their complex structure makes them difficult to study using traditional methods.

The team will train computer models to identify hidden patterns in the glass compositions, revealing how nitrogen alters the material’s structure and properties. The ML model will predict how new glass formulas will perform, saving time, money, and lab experiments.

If successful, this research could revolutionize the design of next-gen amorphous materials, from ultra-durable phone screens to safety glass in jet engines. It’s a small step toward a future where AI and material science collaborate to create the unbreakable.

The project is part of:

What is a seed project?

A seed project is a minor project funded by a knowledge environment or a research group at the university. The aim is to launch and promote excellent research. Depending on the financier, a seed project may be to idenfify new or deepen existing collaborations, preferably cross-disciplinary ones, to explore possible research issues in a feasibility study, to collect empirical material, or to write an application for external funding.