Matthew Rosseinsky

This PhD project at the University of Liverpool offers an exciting opportunity to accelerate the discovery of new materials by combining explainable artificial intelligence (AI) and robotic solid-state synthesis. The research is based in the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry, located at the Materials Innovation Factory—the largest industry-academia colocation in UK physical science. New properties in technology require new material structures, and this project aims to discover such materials by leveraging chemical understanding and advanced computational tools. The student will use automated reasoning and explainable AI methods, recently developed by the supervisory team, to identify promising regions of chemical space for synthesis. These predictions will be explored using a robotic workflow for solid-state chemistry, integrating automated weighing, mixing, high-temperature furnaces, and AI-driven diffraction data analysis. The project is highly interdisciplinary, combining expertise in inorganic materials discovery, synthesis, and characterisation (Prof Matthew Rosseinsky) with interpretable AI tools applied to chemistry and beyond (Prof Katie Atkinson). The student will develop skills in automation, programming (including using and extending explainable AI frameworks), solid-state synthesis, crystallography, and measurement techniques. Training will also cover teamwork, scientific communication, and interdisciplinary collaboration, as computational and experimental researchers work closely together. The research builds on a new materials family recently discovered in Liverpool, notable for its structural complexity and cubic symmetry. The project offers immersion in explainable AI methods, which are essential for advancing out-of-distribution materials discovery. The student will join a team with a proven track record of integrating synthetic chemistry, computation, and AI to discover new functional materials, providing an excellent training environment. Funding is provided through the EPSRC DAMC CDT Studentship, covering full home tuition fees and a maintenance grant for four years (2025-26 rates: £5,006 pa tuition fees, £20,780 pa maintenance grant; 2026-27 rates TBC). A Research Training Support Grant is included for consumables and conference attendance. While EPSRC funding does not cover international fees, limited scholarships are available for outstanding international students to meet the fee difference. Disabled Students’ Allowance may be available for eligible candidates. Applicants should have or expect to obtain a first or upper second class degree (or equivalent) in Chemistry, Materials Science, Physics, Computer Science, or a related discipline. Experience or interest in automation, programming, solid-state synthesis, crystallography, or AI is desirable. The University of Liverpool is committed to diversity and inclusion, supporting reasonable project adaptations for students with caring responsibilities, disabilities, or other personal circumstances. The project is expected to start in October 2026. Interested candidates are encouraged to contact the supervisory team for further information and to review the CDT guide on 'How to Apply.' Applications should be submitted online, indicating Chemistry as the subject area and including the project title and reference number CCPR168. Early application is recommended, as the position will remain open until filled.

This PhD project at the University of Liverpool offers a unique opportunity to accelerate the discovery of new materials by combining explainable artificial intelligence (AI) and robotic solid-state synthesis. The research focuses on synthesising materials with novel crystal structures, targeting promising chemical spaces guided by advanced automated reasoning tools developed by the supervisory team. You will gain hands-on experience in automation, programming, solid-state synthesis, and crystallography, working within an interdisciplinary team that integrates computational and experimental expertise. The project is based on a recently discovered materials family in Liverpool, notable for its structural complexity and cubic symmetry. The use of explainable AI methods is essential for exploring out-of-distribution materials, enabling human researchers to build on these discoveries. The team’s expertise in interpretable AI and digital workflows provides an immersive environment for learning and innovation. Supervised by Professor Matthew Rosseinsky (inorganic materials discovery, synthesis, and characterisation) and Professor Katie Atkinson (interpretable AI tools applied to chemistry), with support from Dr. T Manning, you will benefit from a proven track record of integrating synthetic chemistry, computation, and AI to discover new functional materials. The Materials Innovation Factory, the largest industry-academia colocation in UK physical science, offers state-of-the-art facilities and collaborative opportunities with 35 industrial partners. The EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry (DAMC CDT) provides comprehensive training in robotic, digital, chemical, and physical thinking. The studentship covers 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 additional support through the Disabled Students’ Allowance. Applicants should have a strong academic background in Chemistry, Materials Science, Computer Science, Physics, or related fields, with an interest in automation, programming, solid-state synthesis, crystallography, or AI. The project encourages diversity and inclusivity, offering reasonable adaptations for students with caring responsibilities, disabilities, or other personal circumstances. Applications are reviewed on a rolling basis and the position will be filled as soon as a suitable candidate is found. Early application is strongly recommended. For informal enquiries, contact Dr. Vikki Berryman ([email protected]). Please ensure you include the project title and reference number CCPR168 when applying, indicating Chemistry as the subject area. Review the CDT guide on 'How to Apply' for specific instructions. References supporting the project include recent publications in Angewandte Chemie, Science, and Accounts of Chemical Research, highlighting the innovative approaches and successful outcomes of the supervisory team.

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.

The University of Liverpool is offering a fully funded PhD position as part of the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry (DAMC CDT). This opportunity is ideal for candidates interested in computational chemistry, machine learning, and materials discovery, with a focus on bridging the gap between computational predictions and real-world synthesis of materials. The project aims to make more realistic predictions of material stability at synthesis temperatures by integrating machine learning, thermodynamics, and disorder modelling into traditional computational chemistry methods. Supervised by Dr George Darling and Prof Matthew Rosseinsky, the student will join a collaborative team at the Materials Innovation Factory, the largest industry-academia colocation in UK physical science. The research will involve developing next-generation methods for predicting synthetically accessible structures, improving the accuracy of predictions by capturing finite temperature behaviour and including disordered materials in stability assessments. The project builds on recent advances in digitally targeted discovery and comprehensive description of disorder in crystalline materials. The successful candidate will develop skills in programming, machine learning, solid state and computational chemistry, and scientific communication. The training is cohort-based, with structured support and access to world-class facilities. The PhD programme is designed in collaboration with 35 industrial partners to produce flexible, employable researchers capable of cross-domain communication. Funding includes full home tuition fees, a maintenance grant for 4 years at the UKRI minimum rate (£21,805 for 2026/27), and a Research Training Support Grant for consumables and conferences. Limited scholarships are available to cover the fee difference for outstanding international students. Disabled Students’ Allowance may be available for eligible candidates. Applicants should have, or be due to obtain, a Master’s degree or equivalent in Chemistry, Engineering, Materials Science, Physics, or related disciplines. Exceptional candidates with a First Class undergraduate degree will also be considered. International applicants must meet the minimum English language requirement of IELTS 6.5 overall (no band below 5.5) or equivalent. To apply, candidates are encouraged to contact the supervisory team for informal enquiries, prepare application documents as per the CDT guide, and submit an online application including the project title and reference number CCPR170, indicating Chemistry as the subject area. Applications are reviewed on a rolling basis, and early application is advised as the position may be filled before the deadline of 30th April 2026. For more information, visit the project and supervisor pages or contact Dr George Darling ([email protected]) or Prof Matthew Rosseinsky ([email protected]).

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.