AI-Driven Materials Design for Next-Generation Capacitors The University of Manchester in United Kingdom

AI_CDT_DecisionMaking

Details

Background

The exponential growth in electronics and electrification has created an urgent demand for advanced capacitors, critical components in semiconductors and electric vehicles. With the global capacitor market projected to reach $30 billion by 2026 and each electric vehicle requiring thousands of capacitors, the need for higher-performance dielectric materials has never been more pressing. Traditional material discovery methods, relying on iterative experimentation, are struggling to meet these escalating demands. AI-driven materials design offers a revolutionary approach to address this challenge. By leveraging machine learning models and high-throughput computational screening, this method can rapidly explore vast material composition spaces, optimising for crucial properties like energy density and voltage tolerance. This project aims to develop and apply these advanced AI techniques to discover novel dielectric materials for next-generation capacitors. The research will bridge computational predictions with real-world applications by synthesising and characterising the most promising AI-identified materials. Success in this endeavour could significantly impact various industries, enabling more powerful semiconductors and more efficient electric vehicles. Ultimately, this AI-driven approach to material discovery has the potential to accelerate technological advancements and contribute to the development of more sustainable energy solutions.

Project Aims

This PhD project focuses on enhancing the discovery process for new dielectric materials in high-performance capacitors. It aims to develop AI-driven models that can accurately predict the structure and properties of novel dielectric materials. A crucial part of this project is integrating these models into a high-throughput computational screening framework for material optimisation. This approach enhances traditional material discovery methods by creating predictive and adaptable models, allowing for a more efficient and targeted exploration of potential materials. The AI-driven nature of the discovery process is key to finding the best material compositions and structures to improve the energy density and voltage tolerance of capacitor systems.

Research Methodology

The project combines theoretical development, computational modelling and experimental work. Initially, it will focus on creating and improving probabilistic models that account for uncertainties in structure (e.g., perovskite and tungsten bronze type) dielectric materials and their response to different extreme conditions (e.g., temperature, voltage or frequency). These models are unique because of their differentiable nature, making them a vital part of the simulation process for topology optimisation. This method will allow for an effective exploration of new material compositions with optimised functional properties. Synthesis and characterisation these new discovered materials, such as ceramic synthesis and electrical characterisation, electron microscopy centres at the Department of Materials, Nancy Rothwell Building, is essential to ensure they are effective and applicable in real-world situations.

Desirable Student Background:

The ideal candidate for this project would have a strong STEM background, particularly in physics, materials science, or computer science/mathematics. While expertise in all areas isn't required thanks to the CDT's comprehensive first-year training program, a solid foundation in mathematics is essential for understanding both the material physics and machine learning aspects of the work. Key beneficial skills include understanding of basic materials science concepts and crystal structures, programming ability for implementing machine learning models, and knowledge of statistics and probability for working with probabilistic models. Some familiarity with electrical properties of materials would be helpful but could be developed during training. A computer science graduate with strong ML background could learn the necessary materials science through domain-specific courses, while a materials science graduate could develop their AI expertise through the core ML modules. Most importantly, the candidate should demonstrate enthusiasm for interdisciplinary work and the ability to bridge computational methods with practical applications.

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 ( [email protected] ).

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.

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