Dynamic ML-Driven Design for Next-Gen Material Design

AI_CDT_DecisionMaking

Details

Do you want to be at the forefront of applying machine learning to material science challenges with direct, real-world impact? Join us in a cutting-edge PhD project focused on polymer informatics [1, 2] —where machine learning meets material science to predict and enhance the performance of polymers. 

This research will explore the dynamic nature of polymer binder design optimization for electrochemical applications [3] where binders—though small in proportion—play a critical role in battery stability, durability, and overall lifespan. The challenge lies in designing optimal binders that must satisfy a complex set of physical and chemical properties, from thermal stability and chemical resistance to adhesion strength. Traditional methods rely on trial and error, but our project aims to revolutionize this with a data-driven, ML-optimized approach.

The optimization problem is not static. Polymer binders are governed by a vast array of design variables (e.g., chemical structure, molecular topology), and identifying the most impactful variables for optimization requires a nuanced, flexible approach. You’ll pioneer a dynamic, ML-driven approach where the model learns which design variables to focus on, then expands its decision-making to include new, impactful factors as they emerge (similar to [5,6]). This adaptive decision space means the model isn’t just optimizing—it’s learning and evolving.

Building on previous work of the group [4] you will use multi-objective Bayesian optimization to build a framework that: 

• Combines existing simulation models of the chemistry and topology of polymer binders.
• Adapts in real-time to prioritize key variables like binder structure and chemistry.
• Expands its design space dynamically, adding new variables as data reveals fresh insights.
• Balances competing objectives, such as durability, cost, and sustainability, with every experimental iteration.

This research will combine cutting-edge machine learning with advanced materials science to address a highly complex, real-world problem. As part of this project, you'll be part of an innovative team developing technology that could transform the way materials are optimized, pushing forward advances in battery technology and sustainable energy storage.

Desirable Student Background:

The project would be suited to a student with an excellent undergraduate degree/MSc in Computer Science, Mathematics or a related discipline. Strong mathematical and computational skills are required, and ideally prior experience of working with optimization algorithms. Additional knowledge or an interest in polymer design/chemistry is desirable.

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project. For any questions please contact the UKRI AI Decisions CDT Team (aidecisionscdt@manchester.ac.uk).

How to apply:

Please apply through the below link for the PhD Artificial Intelligence CDT:

https://pgapplication.manchester.ac.uk/psc/apply/EMPLOYEE/SA/s/WEBLIB_ONL_ADM.CIBAA_LOGIN_BT.FieldFormula.IScript_Direct_Login?Key=UMANC1251000021489F

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language Certificate (if applicable)

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. (Equality, diversity and inclusion | The University of Manchester)

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants.