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Towards prediction of synthetically accessible organic molecular crystals


Project Description

Please note that this project will remain open until a successful candidate is found.
We’ll combine new geometric invariants (developed in Kurlin’s group) with known crystals in the Cambridge Structural Database (curated by Pulido and Cole at the Cambridge Crystallographic Data Centre) to substantially accelerate the in silico materials design recently pioneered by Cooper1.
The first sub-goal is to fully leverage the CSD with more than 1M crystals and tackle the challenge of “negative data” (absence of negative examples of “non-existing” crystals), which is a key obstacle for machine learning in crystal structure prediction2.
The second sub-goal is to resolve the embarrassment of “over-prediction” when throwing more computer power only generates more approximate local minima in landscapes of simulated crystals.
The supervisory team has a unique combination of skills in functional materials design and geometric classifications as well as industry collaboration and support to achieve the above goals as follows.
The first milestone (years 1-2) is to geometrically characterise shapes of molecules in known crystals and replace them by new molecules of similar shapes to form synthetically accessible crystals. Our implementation will extend the CSP speculator developed by Pulido and Cole.
The second milestone (years 2-3) is to cluster a landscape of simulated crystals through a continuous hierarchy based on reliable distances between crystals. This clustering will accelerate the prediction of target properties3, because simulations will run on a smaller set of representative crystals.
The completion of this project will automate the functional materials design first for organic molecular crystals and can be extended to other solid crystalline materials such as metal organic frameworks.

Qualifications: Applications are welcomed from students with a 2:1 or higher masters degree or equivalent in Computer Science or Computational Chemistry or related areas. Strong communication skills and programming experience in C++ or Python are essential.#
Informal enquiries should be addressed to Dr Vitaliy Kurlin, , http://kurlin.org.

Please apply by completing the online postgraduate research application form.
Please ensure you quote the following reference on your application: LRC1914 - Towards prediction of synthetically accessible organic molecular crystals

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 3years (£15,009 pa in 2019/20). 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. An offer can be made after the co-funding from the Cambridge Crystallographic Centre is confirmed.

References

1 A. Pulido, et. al. “Functional materials discovery using energy-structure-function maps”, Nature, 2017, 543, 657-664.
2 J-H. D. et. al. “Fine-Tuning of Crystal Packing and Charge Transport Properties of BDOPV Derivatives through Fluorine Substitution” J. Am. Chem. Soc, .2015, 137 (50), 15947-15956.
3. F. Musil, et al. “Machine learning for the structure–energy–property landscapes of molecular crystals”, Chem. Sci., 2018, 9, 1289-1300.

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