Accelerating computational materials discovery with diverse toolsets for verification and optimisation


The discovery of new functional materials to drive technologies for the net zero transition, such as batteries, solar absorbers, rare-earth-free magnets for wind power and a myriad of other unmet needs, is a scientific and societal grand challenge. Recent attempts [1-3] show that reliable automated materials discovery is not currently possible.[4]

Two PhD studentships (1 chemistry, 1 computer science) are available that will tackle the challenge to develop and implement an automated robot-based workflow that will accelerate the materials discovery process. They build on our recent physical science progress in automated synthesis of extended inorganic solids [5] and computer science progress in the diffraction data analysis required to define discovery [6]. The two students will work closely together with a multidisciplinary supervisory team to develop and integrate the methods and tools towards an automated high-throughput workflow that will revolutionise the discovery of functional inorganic materials.

This project, suited to a student with a Computer Science or Mathematics background, will formally define the nature and consequences of the decisions that need to be made in the automated workflow and identify both the optimal combination of existing methods and tools to accelerate discovery and the gaps in capability that currently exist. The student will develop new methods and tools to address those gaps. Their project has the scope to span the entire process from initial suggestion of experimental targets through the autonomous assessment of experimental data produced by the automated workflow to the ultimate definition of experimental success in realising, rather than merely proposing, a new functional material. It offers the student the opportunity to both develop new methods and to participate in implementing them in a new workflow that will change how we find the materials that society will need in the future.

Owing to the multi-faceted nature of this dynamic project, the student will work closely with inorganic chemists, especially the chemistry PhD student on this project, physicists, engineers, material scientists as well as computer scientists leading the PhD supervision, to implement the methods and tools for the discovery of new materials for a variety of applications.

Qualifications: Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Computer Science or Mathematics.  

This position will remain open until a suitable candidate has been found. 


Please apply by completing the online postgraduate research application form here:

How to apply for a PhD - University of Liverpool  

Please ensure you quote the project title on your application.


Open to students worldwide

Funding information

Funded studentship

Applications from candidates meeting the eligibility requirements of the EPSRC are welcome – please refer to the EPSRC website


If this studentship is funded for 3.5 years. It provides full tuition fees and a stipend of approx. £18,622 (this is the rate from 01/10/2023) full time tax free per year for living costs. The stipend costs quoted are for students starting from 1st October 2023 and will rise slightly each year with inflation.

The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.

Due to a change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.



  1. A. Merchant et al., Scaling deep learning for materials discovery, Nature 624, 80–85 (2023).
  2. C. Zeni et al., MatterGen: a generative model for inorganic materials design,
  3. N. J. Szymanski et al., An autonomous laboratory for the accelerated synthesis of novel materials, Nature 624, 86–91 (2023).
  4. Leeman et al., Challenges in high-throughput inorganic material prediction and autonomous synthesis,
  5. Hampson et al., A high throughput synthetic workflow for solid state synthesis of oxides, Chem. Sci., 15, 2640-2647 (2024).
  6. Mirauta et al., High throughput decomposition of spectra, NeurIPS 2023.