Particle properties by design

This interdisciplinary project presents an exciting opportunity for an ambitious scientist or engineer to work across the boundaries of chemistry, physics and engineering, with opportunities to develop a broad portfolio of skills.<br /> <br /> Being able to predictively design particle properties is of great economic value and is applicable to a range of industries such as pharmaceuticals, agrochemical, additives, cosmetics and food. This project aims to develop machine learning models to predict a particle shape and size for a given chemical formulae and crystallisation method. Extractive Language learning models developed will be able to understand crystallographic information from the big data available in the CSD and enable future applications in considering other particle properties. This project will enhance the understanding of the value of metadata that could be associated with structural information and will help define the standards required for crystal structure data curation necessary to deliver Materials 4.0.<br /> <br /> The project will combine data science and structural science work with researchers at Leeds and at the Cambridge Crystallographic Data Centre, and will involve development of Large language modelling to process metadata from structural information. You will also have an opportunity to learn machine learning methods for the analysis of structural information with a view to predict particle properties. You will be funded by the Royce CDT and the Cambridge Crystallographic Data Centre.<br />

<p style="text-align:justify; margin-bottom:11px">Controlling particle properties is key to achieving desired material properties. The stability, downstream processability and bioavailability of a drug is dependent on the particle morphology, and surface characteristics, while electronic and photonic properties of crystals are anisotropic and are dependent on both particle size and the dominant crystal facets. Process control can help achieve property control to some extent, however, it is heavily dependent on the chemical environment and the crystallisability of the material. Hence, being able to predict such properties from big data can minimize experimental needs and can prove to be a more sustainable means to materials discovery.</p> <p>Recently, high throughput microscopy-based machine learning models have been successfully used to identify crystal habit. However, some forms of twinning cannot be identified from microscopic images and hence can skew the ML model. Such approaches would benefit from access to crystallographic information, because such effects will be accounted for during crystal structure modelling and will also be most likely described in the associated literature.</p> <p>The wealth of information in the Cambridge Structural Database can be used to enhance the predictive capabilities with the use of appropriate machine learning models. In this project, machine learning models based on traditional molecular graph sets and attachment energies will be combined with the use of large language models to extract information from the metadata and literature to produce a model with superior particle property prediction capabilities.</p> <p>The project brings together data science, crystallography and surface science. You will receive training on crystallography, and will be able to apply to go to the Durham school of crystallography to gain an in-depth understanding of the crystallographic features required. The student will also receive in-house training on the use of powder X-ray diffraction and angle resolved polarised Raman spectroscopy academic supervisor. Data Science aspect will be self taught with guidance from secondary supervisor as well as through collaborations with the STFC Scientific Machine Learning group.</p> <p>You will have an industrial supervisor from CCDC and will receive additional training in use of CCDC&rsquo;s informatics focused modelling tools and access to domain experts to provide further support for data analysis and curation. You will benefit from access to CCDC&rsquo;s wider scientific network, participation at annual Student Day meetings, and the opportunity for a placement at CCDC&rsquo;s office in Cambridge, UK to help deliver project outcomes.</p> <p><a href="https://www.ccdc.cam.ac.uk/">Cambridge Crystallographic Data Centre</a> (CCDC)</p>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Chemical &amp; Process Engineering</strong></em>, in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Particle properties by design</strong></em> as well as&nbsp;<a href="https://eps.leeds.ac.uk/chemical-engineering/staff/6605/dr-anuradha-r-pallipurath-">Dr Anuradha Pallipurath</a>&nbsp;as your proposed supervisor and <em><strong>in the Finance Section, that the funding source you are applying for is ROYCE CDT.</strong></em></p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. &nbsp;We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 17 May 2024:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p class="MsoNoSpacing" style="text-align:justify">A highly competitive <a href="https://www.royce.ac.uk/cdt/">Royce CDT</a> Studentship in collaboration with the Cambridge Crystallographic Data Centre, consisting of the award of full academic fees, together with a tax-free maintenance grant of &pound;19,237&nbsp;in session 2024/25 per year for 3.5 years.</p> <p class="MsoNoSpacing" style="text-align:justify">This opportunity is open to UK applicants only. All candidates will be placed into the Royce CDT Studentship Competition and selection is based on academic merit.</p> <p class="MsoNoSpacing">Please refer to the&nbsp;<a href="https://www.ukcisa.org.uk/">UKCISA</a>&nbsp;website for&nbsp;information regarding Fee Status for Non-UK Nationals.</p> <p class="MsoNoSpacing" style="text-align:justify">&nbsp;</p>

<p>For further information about this project, please contact Dr Anuradha Pallipurath by email to&nbsp;<a href="mailto:a.r.pallipurath@leeds.ac.uk">a.r.pallipurath@leeds.ac.uk</a>&nbsp;or by telephone to&nbsp;+44 (0)113 343 6401.</p> <p>For further information about your application, please contact Doctoral College Admissions by email to&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p> <p>&nbsp;</p>