Experimental discovery of new Inorganic Materials for Net Zero Technologies

Reference number: CCPR120

Description

The experimental discovery of new inorganic materials shows us how crystal structure and chemical composition control physical and chemical properties. It is therefore critical for our ability to design functional materials with the properties we will need for the net zero transition. Examples include ion motion and redox chemistry in batteries for transport and grid storage, solar absorbers for photovoltaic technologies, rare-earth-free magnets for wind power, catalysts for biomass conversion or water splitting for hydrogen generation, components in low-energy information technology and myriad other unmet needs.

This PhD project will tackle the synthesis in the laboratory of inorganic materials with unique structures that will expand our understanding of how atoms can be arranged in solids. The selection of experimental targets will be informed by artificial intelligence and computational assessment of candidates, working with a multidisciplinary team of researchers to maximise the rate of materials discovery. The resulting materials will be experimentally studied to assess their suitability in a wide range of applications, combining our broad materials characterisation expertise with that of our international industrial and academic collaborators. The student will thus both develop a strong materials synthesis, structural characterisation and measurement skillset, and the ability to work with colleagues across disciplines in a research team using state-of-the-art materials design methodology. The success of this approach is demonstrated in a range of papers involving the discovery of a new lithium solid electrolyte for all solid state batteries (Science 383, 739, 2024), solid electrolytes with high electrochemical stability (Journal of the American Chemical Society 143, 18216, 2021), lithium conducting oxide argyrodites that demonstrate enhanced stability over sulphide materials (Journal of the American Chemical Society 144, 22178, 2022), and the realisation of the lowest ever thermal conductivity of any inorganic crystalline material (Science 373, 1017, 2021). We have recently developed a high throughput solid state synthetic workflow which will further accelerate the discovery of new inorganic oxide materials (Chemical Science 15, 2640, 2014), closing the workflow where initial target selection is informed by artificial intelligence.

The project is based in the Materials Innovation Factory (https://www.liverpool.ac.uk/materials-innovation-factory/) at the University of Liverpool. The project will make use of tools developed in the multi-disciplinary EPSRC Programme Grant: “Digital Navigation of Chemical Space for Function” and the Leverhulme Research Centre for Functional Materials Design, that seek to develop a new approach to materials design and discovery, exploiting machine learning and symbolic artificial intelligence, demonstrated by the realisation of new functional inorganic materials. Examples include the first tools to guarantee the correct prediction of a crystal structure (Nature 68, 619, 2023), and to learn the entirety of known crystalline inorganic materials and guide discovery (Nature Communications 12, 5561, 2021). You will thus gain understanding of how the artificial intelligence and computational methods developed in the team accelerate materials discovery, and be able to contribute to the development of these models, which are designed to incorporate human expertise.

As well as obtaining knowledge and experience in materials synthesis, crystallography and measurement techniques, the student will develop skills in teamwork and scientific communication, as computational and experimental researchers work closely together. There are extensive opportunities to use synchrotron X-ray and neutron scattering facilities.

Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Chemistry, Physics, or Materials Science, particularly those with some of the skills directly relevant to the project outlined above. Experience in structural characterisation of inorganic materials or electron microscopy is an advantage.

Please apply by completing the online postgraduate research application form here: How to apply for a PhD - University of Liverpool 

Please ensure you include the project title and reference number CCPR120 when applying.

We want all of our staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, If you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.

Availability

Open to students worldwide

Funding information

Funded studentship

The funding for this position may be a University of Liverpool School Funded Studentship (SFS) or an EPSRC Doctoral Training Partnership (DTP) studentship. The eligibility details of both are below.

EPSRC eligibility

Applications from candidates meeting the eligibility requirements of the EPSRC are welcome – please refer to the EPSRC website http://www.epsrc.ac.uk/skills/students/help/eligibility/.

If this studentship is funded by the EPSRC DTP scheme and is offered for 3.5 years in total. It provides full tuition fees and a stipend of approx. £19,237 (this is the rate from 01/10/2024) full time tax free per year for living costs. The stipend costs quoted are for students starting from 1st October 2024 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.

EPSRC Studentships are available to any prospective student wishing to apply including international students. Up to 30% of our cohort can comprise of international students and they will not be charged the fee difference between UK and international rate.

Supervisors

References

  1. Han, A. Vasylenko, LM. Daniels, CM. Collins, L. Corti, R. Chen, H. Niu, TD. Manning, D. Antypov, MS. Dyer, J. Lim, M. Zanella, M. Sonni, M. Bahri, H. Jo, Y. Dang, CM. Robertson, F. Blanc, LJ. Hardwick, ND. Browning, JB Claridge, MJ. Rosseinsky, (2024) Superionic lithium transport via multiple coordination environments defined by two-anion packing, Science, 383, 739-745 
  2. Hampson, MP Smith, LL. Arciero, CM. Collins, LM. Daniels, TD. Manning, MW. Gaultois, JB. Claridge, MJ. Rosseinsky, (2024) A high throughput synthetic workflow for solid state synthesis of oxides, Chemical Science, 15, 2640-2647Morscher, BB. Duff, G. Han, LM. Daniels, Y. Dang, M. Zanella, M. Sonni, A. Malik, MS. Dyer, R. Chen, F. Blanc, JB. Claridge, MJ. Rosseinsky, (2022) Control of Ionic Conductivity by Lithium Distribution in Cubic Oxide Argyrodites Li6+xP1–xSixO5Cl, Journal of the American Chemical Society, 144, 22178-22192
  3. J. Gamon, MS. Dyer, BB. Duff, A. Vasylenko, LM. Daniels, M. Zanella, MW. Gaultois, F. Blanc, JB. Claridge, and MJ. Rosseinsky, (2021) Li4.3AlS3.3Cl0.7: A Sulfide–Chloride Lithium Ion Conductor with Highly Disordered Structure and Increased Conductivity, Chem. Mater., 33, 8733-8744
  4. QD. Gibson, T. Zhao, LM. Daniels, HC. Walker, R. Daou, S. Hébert, M. Zanella, MS. Dyer, JB. Claridge, B. Slater, MW. Gaultois, F Corà, J. Alaria, MJ. Rosseinsky, (2021) Low thermal conductivity in a modular inorganic material with bonding anisotropy and mismatch, Science, 373, 1017-1022
  5. A. Vasylenko, J. Gamon, BB. Duff, VV. Gusev, LM. Daniels, M. Zanella, JF. Shin, PM. Sharp, A. Morscher, R. Chen, AR. Neale, LJ. Hardwick, JB. Claridge, F. Blanc, MW. Gaultois, MS. Dyer, MJ. Rosseinsky (2021), Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry, Nat. Commun., 12, 5561
  6. CM. Collins, LM. Daniels, Q. Gibson, MW. Gaultois, M. Moran, R. Feetham, MJ. Pitcher, MS. Dyer, C. Delacotte, M. Zanella, CA. Murray, G. Glodan, O. Perez, D. Pelloquin, TD. Manning, J. Alaria, GR. Darling, JB. Claridge, MJ. Rosseinsky, (2021) Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning. Angew. Chem.-Int. Ed. 60, 2–11
  7. HC. Sansom, G. Longo, AD. Wright, LRV. Buizza, S. Mahesh, B. Wenger, M. Zanella, M. Abdi-Jalebi, MJ. Pitcher, MS. Dyer, TD. Manning, RH. Friend, LM. Herz, HJ. Snaith, JB. Claridge, MJ. Rosseinsky, (2021) Highly Absorbing Lead-Free Semiconductor Cu2AgBiI6 for Photovoltaic Applications from the Quaternary CuI-AgI-BiI3 Phase Space. J. Am. Chem. Soc., 143 (10). 3983 - 3992.