- Type of research degree
- Application deadline
- Ongoing deadline
- Project start date
- Friday 1 October 2021
- Country eligibility
- International (open to all nationalities, including the UK)
- Competition funded
- Dr Bao Nguyen
- School of Chemistry
In this project, the student will develop a Machine Learning approach, in combination with molecular modelling, to predict reactivities of different reactive sites in a starting material against common reagents. While some experimental reactivity scales have previously been developed, the project will focus on extending them to predictions for novel compounds in silico.
<p>Predicting reaction outcome and selectivity is part of the fundamental training of an organic chemist. However, real syntheses often still result in unexpected outcomes when complex molecules with multiple functional groups are involved. Synthetic chemists, with the assistance of modern computational chemistry, can usually explain these observations after the fact. However, predicting them before the experiments remain a critical challenge in the progress of synthetic chemistry toward a fully matured science.</p> <p>In this project, the student will develop a Machine Learning approach, in combination with molecular modelling, to predict reactivities of different reactive sites in a starting material against common reagents. While some experimental reactivity scales have previously been developed, the project will focus on extending them to predictions for novel compounds <em>in silico</em>. This project will deliver:</p> <ul> <li>Computational approaches to quantify electronic and steric properties of a reactive site. </li> <li>Curated databases of reactivity and selectivity in reactions between organic substrates and common reagents.</li> <li>Interpretable Machine Learning models to predict reactivity using the curated databases. </li> </ul> <p>The toolkit will be demonstrated in case studies, including API-relevant synthetic steps, in collaboration with industrial partners in High Value Chemical Manufacture. The student will benefit from the unique expertise of Nguyen group in Physical Organic Chemistry, Computational Chemistry and machine learning in Chemistry.</p> <p>The project is best suited to a student with strong background and interest in organic mechanisms and synthetic chemistry. Prior knowledge of computational chemistry and machine learning is encouraged, but not necessarily required, as training will be provided for these important transferable skills. The student will also benefit from interdisciplinary training and seminar programmes in process chemistry as a member of the <a href="https://www.iprd.leeds.ac.uk/">Institute of Process Research & Development, Leeds</a>.</p> <h5>References</h5> <p> Herbert Mayr’s reactivity parameters: <a href="https://www.cup.lmu.de/oc/mayr/reaktionsdatenbank/">https://www.cup.lmu.de/oc/mayr/reaktionsdatenbank/</a>. <br /> </p>
<p>Formal applications for research degree study should be made online through the <a href="https://eps.leeds.ac.uk/chemistry-research-degrees/doc/apply">University's website</a>. Please state clearly in the research information section that the research degree you wish to be considered for is ‘Predicting Reactivity and Selectivity with Machine Learning and AI’ as well as <a href="mailto:https://eps.leeds.ac.uk/chemistry/staff/4205/dr-bao-nguyen">Dr Bao Nguyen</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>
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.
<p><strong>Self-funding students are welcome to apply.</strong></p> <p><strong>UK students</strong> – The <a href="https://phd.leeds.ac.uk/funding/138-leeds-doctoral-scholarships-2021-january-deadline">Leeds Doctoral Scholarship (January deadline)</a> and the <a href="https://phd.leeds.ac.uk/funding/118-lund-stephenson-clarke-scholarship-2021">Lund Stephenson Clarke Scholarship </a>are available to UK applicants. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>International students</strong> –The <a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">China Scholarship Council - University of Leeds Scholarship</a> is available to nationals of China. The <a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a> is available to support US citizens. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p>
<p>For further information about the application procedure, please contact Doctoral College Admissions by email: <a href="mailto:firstname.lastname@example.org">email@example.com</a> or telephone: + 44 (0) 113 343 5057. </p> <p>For information regarding the project, please contact Dr Bao Nguyen by email: <a href="mailto:EMAIL@leeds.ac.uk">firstname.lastname@example.org</a>, or telephone: +44 (0)113 343 0109.</p>
<h3 class="heading heading--sm">Linked research areas</h3>