Closed-loop design of heat-resistant austenitic alloys through iterative machine learning and c[...]

University of Southampton; United Kingdom; On-site; GBP 30,000 - 45,000; Full time

Job summary

An innovative research opportunity awaits you at a leading university, where you'll integrate machine learning with high-throughput experimentation to revolutionize the discovery of heat-resistant alloys. This PhD project focuses on developing advanced materials for high-temperature reactors, significantly enhancing their structural integrity. You will work with cutting-edge research infrastructure and receive training from experienced experts. This role is perfect for those passionate about interdisciplinary research at the intersection of materials science and data science, offering a chance to make a substantial impact in the field of alloy development.

Benefits

Generous maternity policy

Onsite childcare facilities

Range of benefits for employee well-being

Access to cutting-edge research infrastructure

Qualifications

• Experience in high-throughput experimentation and combinatorial synthesis.
• Familiarity with alloy design principles and phase diagrams.

Responsibilities

• Develop an ML model for predicting new alloy compositions.
• Conduct high-throughput experiments to validate alloy performance.

Skills

Python

MATLAB

High-throughput experimentation

Additive manufacturing

Materials characterization

Education

First-class or upper second-class honours degree

Master’s degree in a relevant field

Tools

CAPHAD

Microscopy

XRD

Job description

Supervisory Team: Prof Bo Chen, Dr Ben Cameron and Dr Samuel Guerin

This PhD project aims to accelerate the discovery of heat-resistant austenitic alloys by integrating machine learning with high-throughput combinatorial experiments. Through iterative design, synthesis, and validation, the project will develop advanced materials for high-temperature reactors, significantly reducing alloy development time and enhancing structural integrity under creep-fatigue conditions.

The long-term structural integrity of steam generators in high-temperature reactors critically depends on the performance of advanced austenitic stainless steels, particularly under creep and creep-fatigue conditions. However, the conventional development of such alloys has relied heavily on trial-and-error exploration of a vast compositional space defined by elements such as Fe, Cr, Ni, Mn, and Mo. Although this approach has led to the development of alloys such as Type 316, Alloy 617, 800H, and 709, the process remains slow, expensive, and inefficient.

This PhD project aims to revolutionise alloy development by establishing an accelerated discovery protocol that integrates machine learning (ML) with combinatorial high-throughput experimentation in a closed-loop framework. The goal is to streamline the identification and validation of new austenitic stainless steels with superior high-temperature performance.

You will initiate the process by developing an ML model trained on a combined dataset of historical alloy performance data and CALPHAD-based high-throughput thermodynamic simulations. This first-generation ML model will be used to predict new alloy compositions, which will then be experimentally validated through a suite of high-throughput experiments. These experimental results will serve as feedback to iteratively retrain the ML model, enhancing its predictive accuracy.

Specifically, three material synthesis routes will be employed to construct compositional libraries: (i) Compositionally graded thin films, deposited onto a metallic substrate using a unique high-throughput physical vapour deposition (HT-PVD) system available at Southampton; (ii) Compositionally graded bulk samples, and (iii) Bulk samples containing discrete alloy compositions, both fabricated using laser-based directed energy deposition (DED) additive manufacturing.

This project is jointly funded by the UK’s National Nuclear Laboratory, and you will be based at the University of Southampton. You will benefit from access to cutting-edge research infrastructure, including the Testing and Structures Research Laboratory (TSRL), the Material Innovation Laboratory, and the Royce Advanced Metals Processing Facility (e.g., BeAM instrument for DED) located at Sheffield. To this end, trainings will be provided by senior experimental experts.

Entry Requirements

• A first-class or upper second-class (2:1) honours degree (or international equivalent) in Materials Science, Metallurgy, Mechanical Engineering, Applied Physics, or a closely related discipline.

• A Master’s degree in a relevant field is desirable but not essential.

• Familiarity with alloy design principles, phase diagrams, and CALPHAD approach.

• Basic programming skills (e.g., Python, MATLAB) for data analysis or machine learning applications.

• Experience in high-throughput experimentation, combinatorial synthesis, or additive manufacturing (e.g., DED).

• Familiarity with materials characterisation techniques (e.g., microscopy, XRD).

• Strong motivation to conduct interdisciplinary research at the interface of materials science, data science, and manufacturing.

• Ability to work independently and as part of a collaborative team.

Closing date: 03 June 2025.Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships.For more information please visit PhD Scholarships | Doctoral College | University of SouthamptonFunding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

50% match funding (up to a maximum value of £45k over the duration of a 3.5 years PhD) secured from the National Nuclear Laboratory

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk) Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD Eng & Env (Full time)”. In Section 2 of the application form you should insert the name of the supervisor NAME

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: [e-mail]

The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.