AI-assisted alloy design to achieve superior performance

The mechanical properties of metal alloys depend on their chemical composition and process. Because of the large number of alloying elements that can be added to metals such as aluminium and steel, the number of possible chemical compositions of alloys is almost indefinite. This makes it extremely difficult to identify/discover the compositions that maximize the performance of alloys. In the past century, new alloy design and development primarily relies on modification of existing alloys and/or through the error and trial approach. Hence, in the last decades, there have been no real groundbreaking achievements in this area. The rapid growth and widening of applications of machine learning (ML) to engineering and scientific research make such breakthrough work possible. With the currently limited data (composition, process and properties) of alloys, ML models can be trained to establish solid and reliable relationships between the properties and the composition and process of alloys. Then, new alloys and processes can be designed with the required properties in terms of such relationships.

The PhD student will work on two Baosteel-funded projects to use ML to design new aluminium alloys and new steels with improved mechanical properties. The candidate is expected to have either a materials engineering or mechanical engineering background. It is also preferring to have knowledge of or experience in or have the capability to learn machine learning models with basic programming skills.

The School of Mechanical and Mining Engineering at UQ has historical strengths in materials research and has been internationally recognized as one of the world’s leading research institutions on metallic materials and manufacturing.  The AMPAM Centre has provides a unique platform for both materials and manufacturing research. There are ongoing ARC Discovery and/or linkage projects supporting this research. UQ Materials Engineering received the ARC ERA rating of 5 in all years. The SoMME hosts the largest laboratory foundry in Australia, which can ensure the success of the proposed project.  There is also a well-equipped mechanical testing laboratory and a metallographic laboratory.  In addition, UQ also has a world-class Centre for Electron Microscopy and Microanalysis that can be accessed by the candidate.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

Your application will be assessed on a competitive basis.

We take into account your:

• previous academic record
• publication record
• honours and awards
• employment history

A working knowledge of metallic materials would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field/s of materials/mechanical, and chemical engineering and the potential for scholastic success.

A background or knowledge of machine learning is highly desirable.

Before you apply

Before submitting an application you should:

1. check your eligibility for a Doctor of Philosophy (PhD)
2. prepare your documentation
3. contact Professor Mingxing Zhang (mingxing.zhang@uq.edu.au) to discuss your interest and suitability
4. submit your application by 7 April, 2025 7 April, 2025.

When you apply

You apply for this scholarship when you submit an application for your program. You don’t need to submit a separate scholarship application.

In your application ensure that under the ‘Scholarships and collaborative study’ section you select:

• ‘My higher degree is not collaborative’
• ‘I am applying for, or have been awarded a scholarship or sponsorship'
• ‘Other’, then ‘Research Project Scholarship’ and in the ‘Name of scholarship’ field enter ALLOY-ZHANG.

Apply now