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Prof Nigel Berry
No more applications being accepted
Funded PhD Project (European/UK Students Only)
About the Project
This is an excellent opportunity for a well-motivated student to participate in a project funded by the Department of Chemistry (https://www.liverpool.ac.uk/chemistry/) and the Leverhulme Research Centre for Functional Material Design (https://www.liverpool.ac.uk/leverhulme-research-centre/) at the University of Liverpool. The student will be based at the University of Liverpool, with a supervisory team from the department of Chemistry (Dr N. Berry) and will work closely with colleagues in the School of Chemistry at the University of Glasgow (Prof. D. Adams). The studentship is fully funded for a period of 42 months starting in October 2018.
Low molecular weight gels are currently being investigated for a range of interesting applications from optoelectronics to tissue engineering. The gels are a result of the self-assembly of small molecules into long fibres, which then entangle to form the gel matrix. Whilst these gels are extremely interesting, a major limitation is that it is enormously difficult to design the materials.
First, there are very limited design rules for molecules that can self-assemble to form gels. This lack of knowledge means that most gelators are found by accident and new analogues prepared by synthetic iteration, not always with great success. To tackle this, we recently published a computational descriptor-based approach to predicting the ability of molecule to form a gel. With the limitations of a restricted phase space, this was a very successful strategy and we are now using these models to guide our synthetic targets.
Second, not all gels have the same properties. The materials have widely differing stiffnesses, ability to recover after damage, stretchability etc. Assuming that one has found a molecule that can form a gel, it is still extremely difficult to predict in advance what properties the gels will have. Hence, to prepare gels for a specific application, one needs to find both a molecule that can form a gel, and form a gel with the desired properties. We have recently shown that the gel properties can be varied by changing how the gels are prepared. It is therefore perhaps more tractable to find a robust, effective gelator and then permutate how the gels are formed. What is really needed now is therefore a predictive tool for what the gel properties will be. This is the aim of this project.
The predictive tool will be realized by developing robust quantitative structure–property relationships (QSPR) models which link measured properties to compound chemical structure. QSPR is based on the principle that experimentally measured endpoints are a function of molecular properties, encoded numerically as descriptors, which capture the chemical information of the molecule for computational processes. Statistical and machine learning methods (e.g. random forests), will be deployed to link these descriptors to the measured endpoint, i.e. gelation, stiffness, etc. QSPR models will shed light on the key molecular characteristics that are linked to the gelation properties of a compound and also, enable rapid computational screening of libraries to identify candidates that are likely to possess the desired gelation properties.
Qualifications: Candidates should have a good (at least 2.1) degree in chemistry, computer science, materials science or related subject.
Please apply by completing the online postgraduate research application form here: https://www.liverpool.ac.uk/study/postgraduate-taught/applying/online/
Please ensure you quote the following reference on your application: Computational Prediction of Gel Properties (Reference Berry LRC116)
Low molecular weight gels are currently being investigated for a range of interesting applications from optoelectronics to tissue engineering. The gels are a result of the self-assembly of small molecules into long fibres, which then entangle to form the gel matrix. Whilst these gels are extremely interesting, a major limitation is that it is enormously difficult to design the materials.
First, there are very limited design rules for molecules that can self-assemble to form gels. This lack of knowledge means that most gelators are found by accident and new analogues prepared by synthetic iteration, not always with great success. To tackle this, we recently published a computational descriptor-based approach to predicting the ability of molecule to form a gel. With the limitations of a restricted phase space, this was a very successful strategy and we are now using these models to guide our synthetic targets.
Second, not all gels have the same properties. The materials have widely differing stiffnesses, ability to recover after damage, stretchability etc. Assuming that one has found a molecule that can form a gel, it is still extremely difficult to predict in advance what properties the gels will have. Hence, to prepare gels for a specific application, one needs to find both a molecule that can form a gel, and form a gel with the desired properties. We have recently shown that the gel properties can be varied by changing how the gels are prepared. It is therefore perhaps more tractable to find a robust, effective gelator and then permutate how the gels are formed. What is really needed now is therefore a predictive tool for what the gel properties will be. This is the aim of this project.
The predictive tool will be realized by developing robust quantitative structure–property relationships (QSPR) models which link measured properties to compound chemical structure. QSPR is based on the principle that experimentally measured endpoints are a function of molecular properties, encoded numerically as descriptors, which capture the chemical information of the molecule for computational processes. Statistical and machine learning methods (e.g. random forests), will be deployed to link these descriptors to the measured endpoint, i.e. gelation, stiffness, etc. QSPR models will shed light on the key molecular characteristics that are linked to the gelation properties of a compound and also, enable rapid computational screening of libraries to identify candidates that are likely to possess the desired gelation properties.
Qualifications: Candidates should have a good (at least 2.1) degree in chemistry, computer science, materials science or related subject.
Please apply by completing the online postgraduate research application form here: https://www.liverpool.ac.uk/study/postgraduate-taught/applying/online/
Please ensure you quote the following reference on your application: Computational Prediction of Gel Properties (Reference Berry LRC116)
Funding Notes
The award is primarily available to students resident in the UK/EU and will pay full tuition fees and a maintenance grant for 3.5 years (£14,553 pa in 2017/18). Non-EU nationals are not eligible for this position and applications from non-EU candidates will not be considered unless you have your own funding.
Please note that this is a PhD Graduate Teaching Assistantships (GTA) and as such will have teaching commitments (144 hours of contact time per year) and contractual obligations to teaching associated with it.
Please note that this is a PhD Graduate Teaching Assistantships (GTA) and as such will have teaching commitments (144 hours of contact time per year) and contractual obligations to teaching associated with it.
References
Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints, Chem. Sci., 2016,7, 4713-4719
http://pubs.rsc.org/en/content/articlelanding/2016/sc/c6sc00722h#!divAbstract
http://pubs.rsc.org/en/content/articlelanding/2016/sc/c6sc00722h#!divAbstract
Project supervisors
Career overview
Professor Neil Berry is a graduate of Exeter College, University of Oxford, where he obtained his MChem in Chemistry in 1999. He continued his studies at Oxford, earning a DPhil in 2002 under the supervision of Professor Paul Beer. In 2003, he joined the University of Liverpool as a post-doctoral research fellow in computational chemistry, a position funded by Romark L.C. In 2005, he was appointed as a University Teacher in Organic Chemistry, while also continuing his research activities. His academic career progressed with his appointment as a University Lecturer in 2007, promotion to Senior Lecturer in 2012, elevation to Reader in 2017, and finally being appointed as a Professor in 2019. Professor Berry''s research group employs computational modelling techniques, including artificial intelligence, machine learning, and molecular modelling, to address various problems in chemistry, focusing on medicinal chemistry, materials chemistry, reaction mechanisms, and chemoinformatics to enhance the understanding of chemical systems and predict molecular properties.
Research interests
Professor Berry''s research focuses on computational chemistry, employing computational modelling techniques such as artificial intelligence, machine learning, and molecular modelling to address various problems in chemistry. His group''s research activities span several fields, including medicinal chemistry, materials chemistry, reaction mechanisms, and cheminformatics. The aim is to enhance the understanding of chemical systems and to predict molecular properties more accurately.
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