Introducing Temperature and Disorder into Digital Materials Discovery Workflows

This PhD project at the University of Liverpool aims to revolutionize digital materials discovery by bridging the gap between computational predictions and real-world synthesis. You will join a collaborative, multidisciplinary team working at the forefront of materials science, integrating machine learning, thermodynamics, and disorder modelling into traditional computational chemistry workflows. The research addresses a major challenge in the field: improving the accuracy of predictions for the stability of materials at synthesis temperatures, moving beyond energy calculations to more realistic free energy assessments that account for finite temperature behavior and disorder. As a student, you will develop next-generation methods for crystal structure prediction, combining computational chemistry tools with advanced machine learning models. The project builds on recent achievements in digitally targeted discovery and comprehensive disorder description in crystalline materials, providing a unique route to calculating entropy and assessing stability. You will benefit from close collaboration with both computational and experimental researchers, enabling iterative refinement of methodologies through feedback from synthetic outcomes and the use of explainable AI. The supervisory team consists of Dr George Darling, an expert in thermodynamics, crystal structure prediction, and machine learning, and Prof Matthew Rosseinsky, who specializes in disorder, integrated materials discovery workflows, and theory-experiment feedback loops. Their combined expertise ensures robust development of new computational models and materials design hypotheses. The broader research group includes experts who will experimentally test predictions and incorporate new methods into AI-driven discovery workflows. This position is offered under the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry, based in the Materials Innovation Factory—the largest industry-academia colocation in UK physical science. The successful candidate will receive comprehensive training in robotic, digital, chemical, and physical thinking, preparing them for domain-specific research in materials design, discovery, and processing. The PhD program is developed with 35 industrial partners, aiming to produce flexible, employable, and enterprising researchers with strong interdisciplinary communication skills. Funding is provided through the EPSRC DAMC CDT Studentship, covering full home tuition fees and a maintenance grant for four years, with additional support for research consumables and conference attendance. Outstanding international students may be eligible for scholarships to cover the fee difference, and candidates with disabilities may access further support through the Disabled Students’ Allowance. Applicants should have a strong academic background in Chemistry, Physics, Materials Science, or related disciplines, with experience or interest in computational chemistry, machine learning, programming, and solid state chemistry. The project is expected to start in October 2026, and candidates are encouraged to apply early. The University of Liverpool is committed to diversity and inclusion, supporting reasonable project adaptations for students with caring responsibilities, disabilities, or other personal circumstances. For informal enquiries, contact Dr George Darling at [email protected]. Please review the CDT guide on 'How to Apply' and ensure you include the project title and reference number CCPR170 in your application. Apply online via the University of Liverpool portal, indicating Chemistry as the subject area.