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Computational materials design and discovery

Project Description

This project will suit applicants with interests in the development and application of new methods for the computational understanding and prediction of new materials. This includes the development of new methods for crystal structure prediction, development and application of electronic structure calculations to understand electronic and optical properties in organic, inorganic and hybrid materials, and the design and application of high-throughput screening approaches to identify materials with properties such as solar absorption, gas separation and storage, electronic and ionic transport, magnetism and electronic structure. These activities take place within the strong computational chemistry activity in Liverpool, for example in the groups of Dyer, Darling, Troisi and Berry.

Opportunities are available for candidates with strong academic backgrounds with interests across these areas: students will be aligned with potential supervisors both through assessment of fit given student background and the expressed project interests of the student, so there is no need to express a detailed preference at the point of application.

These projects form part of the Doctoral Training Centre for Next-Generation Materials Chemistry The University of Liverpool is offering 8 Ph.D positions starting October 2019 in this new Centre that will deliver a new cross-disciplinary approach to materials chemistry research. The Centre will train PhD graduates at the interface of physical science, AI, data science, and robotics to create the leaders in data-enabled science that UK industry and academia requires to deliver R&D 4.0. We seek applicants with a strong undergraduate background in chemistry, computer science, engineering, physics, mathematics or materials science for these posts.

The 42 month Ph.D projects will tackle multidisciplinary problems which are co-defined by industrial partners, working with University of Liverpool academics in the physical sciences, computer science, and engineering, with supervisory teams spanning the range of disciplines required to tackle the research problems. Core training in robotics, automation and data science will form part of a unifying curriculum, together with leadership and entrepreneurship training, to underpin the individual research projects.

Students in the Doctoral Training Centre for Next-Generation Materials Chemistry ( will be located in the newly opened Materials Innovation Factory (MIF -, which collocates academic and industrial researchers over 4 floors, with state-of-the-art automated research capabilities, including the £3M Formulation Engine. They will benefit from the cross-disciplinary training environment of the MIF, which contains staff from Physics and Computer Science as well as Chemistry, and the well-established community around the Leverhulme Research Centre in Functional Materials Design (, which is typified by a vibrant functioning engagement between physical science and computer science. Industrial partners in the Doctoral Training Centre include Unilever, Johnson Matthey and NSG Pilkington.

The Materials Innovation Factory and Leverhulme Research Centre both reflect the use of computational materials chemistry to accelerate this materials discovery process, leading to the synthesis of a range of novel materials with a variety of functional properties – examples are given in references. The successful candidate will participate in this activity, using their computational skills in close collaboration with experimental groups, to accelerate the discovery of new materials across the full range of materials types and computational approaches.

Name and email address to direct enquiries to:

Informal enquiries should be addressed to Troy Manning ().

Tel. No. for Enquiries: +44(0)151 794 3563

Please apply by completing the online postgraduate research application form here:
Please ensure you quote the following reference on your application: University of Liverpool Doctoral Training Centre in Next-Generation Materials Chemistry CDT02
Applications should be made as soon as possible.

Supervisors may be subject to change.

Funding Notes

The award will pay full tuition fees and a maintenance grant for 3.5 years. The maintenance grant will be £15,009 pa for 2019-20. The award will pay full home/EU tuition fees and a maintenance grant for 3.5 years. Non-EU applicants may have to contribute to the higher non-EU overseas fee. One of the positions will have a requirement to work up to 88 hours/year in teaching-related activity in the Department of Chemistry and may be asked to teach up to 144 hours per year if required, with teaching above 88 hours being paid at the standard University demonstrator rate.


C Collins, M S Dyer, M J Pitcher, G F S Whitehead, M Zanella, P Mandal, J B Claridge, G R Darling, & M J Rosseinsky, Accelerated discovery of two crystal structure types in a complex inorganic phase field, Nature 546 (2017) 280-284

A Landi, A Troisi Rapid evaluation of Dynamic Electronic Disorder in Molecular Semiconductors. Journal of Physical Chemistry 122 (Aug 16 2018) Issue: 32, 18336-18345

Stoner, JL; Murgatroyd, Philip A. E.; O'Sullivan, Morita; Dyer, MS; Manning, TD; Claridge, JB; Rosseinsky, MJ; Alaria, J, Chemical Control of Correlated Metals as Transparent Conductors. Advanced Functional Materials (Mar 14 2019)

Katsoulidis, Alexandros P.; Antypov, Dmytro; Whitehead, George F. S.; et al. Chemical control of structure and guest uptake by a conformationally mobile porous material. Nature 565(Jan 10 2019) Issue: 7738, Pages: 213-+

Q D Gibson, M S Dyer, G F S Whitehead, J Alaria, M J Pitcher, H J Edwards, J B Claridge, M Zanella, K Dawson, T D Manning, et al. Bi4O4Cu1.7Se2.7Cl0.3: Intergrowth of BiOCuSe and Bi2O2Se Stabilized by the Addition of a Third Anion, J. Am. Chem. Soc. 139 (2017) 15568-15571

H C Sansom, G F S Whitehead, M S Dyer, M Zanella, T D Manning, M J Pitcher, T J Whittles, V R Dhanak, J Alaria, J B Claridge, et al., AgBiI4 as a Lead-Free Solar Absorber with Potential Application in Photovoltaics, Chem. Mater. 29 (2017) 1538-1549

C Collins, G Darling, M Rosseinsky. The Flexible Unit Structure Engine (FUSE) for probe structure-based composition prediction. (1 OCT 2018)

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