Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740849
Title: Crystallization properties of molecular materials : prediction and rule extraction by machine learning
Author: Wicker, Jerome
ISNI:       0000 0004 7229 3930
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Abstract:
Crystallization is an increasingly important process in a variety of applications from drug development to single crystal X-ray diffraction structure determination. However, while there is a good deal of research into prediction of molecular crystal structure, the factors that cause a molecule to be crystallizable have so far remained poorly understood. The aim of this project was to answer the seemingly straightforward question: can we predict how easily a molecule will crystallize? The Cambridge Structural Database contains almost a million examples of materials from the scientific literature that have crystallized. Models for the prediction of crystallization propensity of organic molecular materials were developed by training machine learning algorithms on carefully curated sets of molecules which are either observed or not observed to crystallize, extracted from a database of commercially available molecules. The models were validated computationally and experimentally, while feature extraction methods and high resolution powder diffraction studies were used to understand the molecular and structural features that determine the ease of crystallization. This led to the development of a new molecular descriptor which encodes information about the conformational flexibility of a molecule. The best models gave error rates of less than 5% for both cross-validation data and previously-unseen test data, demonstrating that crystallization propensity can be predicted with a high degree of accuracy. Molecular size, flexibility and nitrogen atom environments were found to be the most influential factors in determining the ease of crystallization, while microstructural features determined by powder diffraction showed almost no correlation with the model predictions. Further predictions on co-crystals show scope for extending the methodology to other relevant applications.
Supervisor: Cooper, Richard ; David, Bill Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.740849  DOI: Not available
Keywords: Crystal growth ; Machine Learning ; Crystallization ; Powder diffraction
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