PhD theses

(1093 Words, 7 Minutes)

Nicholas Francia (2022)

Reducing crystal structure overprediction: from small rigid molecules to conformationally complex drugs

In the pharmaceutical industry, the control of a new drug’s crystal form is key to optimise its formulation and mode of action. Computational Crystal Structure Prediction (CSP) methods for organic crystalline materials are becoming increasingly accurate at predicting the relative stability between packings, even if they usually grossly overestimate the number of polymorphs. The purpose of this work is to develop a systematic and scalable method to reduce CSP sets to a small number of putative polymorphs by including temperature effects. In fact, not all hypothetical structures corresponding to local minima in the lattice energy landscape are expected to be stable at finite temperature with many of these that merge into a smaller set of persistent states. In order to identify persistent structures, classical molecular dynamics simulations at finite temperature are performed on CSP-generated crystal structures. Unstable structures are thus automatically removed by checking if molecules exhibit a random inter-molecular orientation, typical of the melted state. On the other hand, to identify those structures that convert to the same geometry, I devised a clustering analysis based on probabilistic fingerprints that provide information on the relative position, relative orientation and conformation of molecules within a dynamic crystal supercell. These molecule-specific fingerprints are able to efficiently distinguish different structures of large supercells and can handle robustly the displacement of atomic positions from equilibrium typical of finite-temperature simulations. These are used to quantitatively assess the similarity between pairs of structures and cluster analogous geometries. Finally, I used Well-Tempered Metadynamics on the cluster centres to overcome MD limits and sample possible slow transitions. I applied this method on molecules of increasingly conformational complexity and datasets spanning from a few dozens to thousands of structures. Instrumental in achieving scalability over a large set of crystal structures has been the development of a Python library that handles the setup of MD simulations and automatically analyses the resulting trajectories, enabling us to manage the large sets of structures typical of real-world CSP applications.

Loukas Kollias (2020)

Molecular modelling of the early stages of Metal-Organic Framework self-assembly

Metal-Organic Frameworks (MOFs) constitute a class of novel hierarchical materials. MOFs are strong candidates in several applications including catalysis, carbon capture and storage and drug delivery. Past and current research on MOFs has utilised several experimental techniques. Nevertheless, a thorough investigation of MOF synthesis requires molecular simulations in order to provide information at length scales unreachable scale for experimental techniques and thus understand the mechanisms of assembly. The purpose of this research project is to study the formation of the MIL-101(Cr) structural building units (SBU) using Molecular Dynamics. A bottom-up approach of assembly is followed starting from the evaluation of half-SBU conformational flexibility in solution. SBU association-dissociation and rearrangement are then assessed leading to a connection between synthesis conditions and the configuration of small scale adducts at an early stage of assembly. In particular the effect of ions (Na+, F-) and solvent (water, DMF) on promoting crystal– like configurations of SBUs is investigated. The enthalpic and entropic contributions are also calculated under various conditions leading to a better understanding on the thermostructural behaviour of the conformers. Finally, the collective behaviour of SBUs during assembly is analysed through simulations in which numerous half– SBUs interact and form clusters. In summary, this work provides a molecular-level understanding to the experimental finding that ions favour crystallinity in MOFs. Ultimately, the conformational complexity in early stages of MOF self-assembly leads to the conclusion that guest molecules that affect the entropic landscape of MOF precursors are key in order to regulate the extent of defects in a MOF cluster.

Veselina Marinova (2020)

Molecular-Level Characterisation of Crystal-Solution Interfaces

The shape of solution-grown crystal particles is largely dependent on the relative growth rate of the morphologically dominant crystal faces, which is known to be affected by the solvent. Developing accurate models for predicting crystal morphologies requires a molecular-level understanding of the solid-liquid interface. Using a combination of molecular dynamics simulations and enhanced sampling methods, this work carries out a comprehensive study on the dynamics and thermodynamics of crystal-solution interfaces for the case of ibuprofen, focusing on aspects often neglected in mesoscopic models for crystal growth. An investigation on the conformational isomerism of ibuprofen shows that conformational rearrangements at the crystal-solution interface are governed by specific surface-solvent interactions and can have a non-negligible impact on the surface growth/dissolution kinetics. An unsupervised clustering algorithm is proposed to extend the study of conformational isomerism for systems with a large number of conformationally relevant degrees of freedom. By assessing thermodynamic and kinetic information on the solvent in contact with crystal surfaces, surface-solvent interactions are found to be solvent- and face-specific. Following this analysis, a computational screening procedure is proposed for identifying solvents which can significantly affect the relative growth rate of the crystal facets and hence, the growth morphology of the crystal. To gain an in-depth understanding into the role of the solvent on the ease of association/dissociation of solute molecules at the crystal surface, a study on the formation of a vacancy on the morphologically dominant crystal faces of ibuprofen is carried out. Thermodynamics of the process reveal a distinct solvent-dependency for several faces, indicating in such cases desolvation-dominated defect formation. The research subject of this dissertation contributes to developing general and computationally-affordable workflows necessary to obtain a comprehensive and quantitative understanding of molecular processes, impacting the solid-liquid interface, which will contribute towards the formulation of detailed mesoscopic growth and dissolution models.

Ilaria Gimondi (2020)

A molecular modelling journey from packing to conformational polymorphism

The efficient and reproducible crystallisation of a polymorph showing the desired properties and functionalities is crucial in a variety of fields, such as the pharmaceutical sector. Characterising thermodynamics and mechanisms of polymorphic transitions at the molecular level is thus a key step towards developing a rational design of crystallisation processes and products. Despite its relevance, a systematic computational analysis of polymorphism and polymorphic transitions still represents a major challenge. In this thesis, metadynamics is employed in combination with state-of-the-art techniques, such as committor analysis and Markov State Models, to provide insight into polymorphism in molecular systems. The first part of the work focuses on packing polymorphism. The investigation of the transition between phases I and III in bulk carbon dioxide aims at testing a set of computational tools able to characterise in detail thermodynamics and mechanism of polymorphic transitions. This set-up is then applied and further developed for the study of CO2 confined in cylindrical nanopores, unveiling a complex landscape of ordered structures, unaccessible in unconfined conditions. Next, the serendipitous and irreproducible discovery of a new polymorph of succinic acid, γ, provides a challenging context to tackle the study of conformational polymorphism. Form γ presents folded conformers in its unit cell, while the other known polymorphs show planar molecules. From molecular dynamics and metadynamics, γ appears labile and metastable, a characteristic that might hinder its crystallisation. The study of the conformational behaviour of succinic acid in water reveals fast interconversions within a network of nine conformers, both folded and planar, among which the folded conformation observed in γ is the most thermodynamically stable. The high flexibility of this molecule is relevant in determining the nucleation mechanism. Simulations of supersaturated solutions and of crystal seeds dissolution suggest that nucleation cannot be classical, but it is rather likely to be a multi-step process.