Introducing Temperature and Disorder into Digital Materials Discovery Workflows University of Liverpool in United Kingdom

This PhD project at the University of Liverpool aims to bridge the gap between computational predictions and real-world synthesis in materials discovery. You will join a collaborative team working at the forefront of digital and experimental materials chemistry, focusing on making more realistic predictions of material stability at synthesis temperatures. The research integrates machine learning, thermodynamics, and disorder modelling into traditional computational chemistry methods to improve the accuracy of crystal structure predictions.

New materials are essential for technological progress, and this project addresses a major challenge: translating computational predictions into experimentally accessible structures. You will develop next-generation methods by combining machine learning and thermodynamic modelling, moving beyond energy calculations to include free energy and finite temperature behaviour. The project builds on recent achievements in digitally targeted discovery and comprehensive disorder description in crystalline materials, providing a unique route to entropy calculation.

The student will work within an integrated team of computational and experimental researchers, benefiting from close collaboration and a feedback loop based on synthetic outcomes. This environment supports methodology refinement and the use of explainable AI. You will gain skills in teamwork, scientific communication, programming, machine learning, solid state and computational chemistry techniques.

The supervisory team includes Dr. George Darling, an expert in thermodynamics, crystal structure prediction, and machine learning, and Prof. Matthew Rosseinsky, who specializes in disorder and integrated workflows for materials discovery. Together, they offer a robust platform for iterative improvement of computational models and materials design hypotheses. The broader team will experimentally test predictions and incorporate new methods into explainable AI-driven discovery workflows.

The project is offered under the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry, based in the Materials Innovation Factory at the University of Liverpool—the largest industry-academia colocation in UK physical science. The successful candidate will receive training in robotic, digital, chemical, and physical thinking, applying these skills in domain-specific research in materials design, discovery, and processing. The PhD training is developed with 35 industrial partners to produce flexible, employable, and enterprising researchers who can communicate across domains.

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 consumables and conference attendance. Outstanding international students may receive scholarships to cover fee differences, and candidates with disabilities may be eligible for a Disabled Students’ Allowance.

Applications are accepted year-round, with interviews conducted on a rolling basis. Early application is advised as the position may be filled before the official deadline. To apply, register online, include the project title and reference number CCPR170, and indicate Chemistry as the subject area. Informal enquiries are encouraged and can be sent to [email protected]. The University of Liverpool is committed to diversity and inclusion, supporting reasonable project adaptations for students with caring responsibilities, disabilities, or other personal circumstances.