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 advance the accuracy of crystal structure prediction workflows.

New materials are essential for technological progress, and this project addresses a major challenge: improving the predictive power of computational models for experimental synthesis. You will develop next-generation methods by combining machine learning and thermodynamic modelling to predict synthetically accessible structures with greater accuracy. The project moves beyond conventional energy calculations by incorporating free energy and finite temperature behaviour, including the assessment of disordered materials.

Building on achievements in digitally targeted discovery and comprehensive disorder description in crystalline materials, the student will join an integrated team of computational and experimental researchers. Close collaboration and feedback loops based on synthetic outcomes will enable methodology refinement, including the use of explainable AI. You will develop skills in teamwork, scientific communication, programming, machine learning, solid state and computational chemistry techniques.

The supervisory team includes Dr. George Darling, with expertise in thermodynamics, crystal structure prediction, and machine learning, and Prof. Matthew Rosseinsky, specialising in disorder, integrated workflows for materials discovery, and theory-experiment feedback loops. The team has demonstrated integrated ML/computational chemistry pathways and a unique perspective on disorder in crystalline materials, providing a new route to entropy calculation.

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 benefit from training in robotic, digital, chemical, and physical thinking, applied to domain-specific research in materials design, discovery, and processing. PhD training is developed with 35 industrial partners to generate flexible, employable, 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 4 years, with additional support for consumables and conferences. International students may apply, with limited scholarships available to cover fee differences. Disabled Students’ Allowance is available for eligible candidates.

Applicants should have a strong background in chemistry, materials science, physics, or a related discipline. Experience with computational methods, programming, machine learning, or solid state chemistry is desirable. The position is open to both home and international students. Candidates with disabilities may be eligible for additional support. Applicants should review the CDT guide on 'How to Apply' as the process may differ from standard applications. Early application is advised due to rolling interviews.

To apply, register and submit your application online, including the project title and reference number CCPR170, indicating Chemistry as the subject area. Informal enquiries are encouraged and can be directed to Dr. Darling at [email protected]. Interviews are conducted on a rolling basis, and the position will be filled as soon as a suitable candidate is found.

The University of Liverpool is committed to diversity and inclusion, supporting reasonable project adaptations for students with caring responsibilities, disabilities, or other personal circumstances.