Characterising the energy landscapes of molecular organic crystals

Abstract

This thesis describes the development of methods for exploring, characterising, and fitting the energy landscapes of molecular crystals. A precise and comprehensive understanding of the energy landscapes of molecular crystals is essential for accurately predicting observed crystal structures and their behaviour. This is emphasised early through describing our efforts to predict the crystal structure of target XXVII from the recent blind test. Thereafter, using threshold Monte Carlo simulations and empirical force fields, we investigate sampling crystal landscapes of different compounds revealing key insights into general trends. In particular, we implement and test adaptive sampling based on monitoring the number of unique, energy minimised structures, finding considerable improvement over fixed sampling, especially at higher energies. Additionally, having noted that low energy connections can be converged with relative ease by the threshold algorithm, a method for reducing overprediction in organic crystal structure prediction is developed wherein low energy connections around predicted structures are sampled and then the landscape is clustered to the minimum energy structure in each of the resulting energy basins. This approach is tested on predicted landscapes for rigid and flexible molecules, with the results showing it can considerably reduce overprediction without discarding matches to experimentally observed structures. In the second half of the thesis studies into efficiently fitting the energy landscapes of organic crystals using machine learning potentials are detailed. These involve active learning from low-level predicted landscapes as well as on-the-fly training within Monte Carlo simulations. Potentials trained with these methods on a variety of compounds are shown to achieve excellent accuracy relative to high-level quantum chemistry calculations while maintaining low computational costs. Further case studies investigate extending these potentials to improve transferability between compounds and for achieving high-level geometry optimisations.

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Identifiers

ORCID for Graeme Day: orcid.org/0000-0001-8396-2771

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