Crystal Engineering for Accelerated Material Design
This research theme aims to build upon the work performed above and, on the work, already done by the MacGillivray group. The goal here is to take all the data acquired by the MacGillivray group and place it under a single domain for crystal engineering to aide in accelerated materials design. It is important for work in crystal engineering to gravitate towards predictions of crystal structures and corresponding solid-state properties. Doing so can reduce needs for laboratory experiments (e.g. organic synthesis, crystal growth), with major benefits including time, finances, and further reduction of chemical waste.
The MacGillivray group aims to examine crystal engineering work that involves automated organic synthesis combined with computational chemistry and theoretical chemistry (e.g. machine learning), as well as high throughout methods for solid state characterization (e.g. X-ray diffraction). The MacGillivray group has laid important groundwork in crystal engineering work to allow for efforts in crystal structure prediction and machined learning to address how to control reactions in crystals, with highly feasible and immediate opportunities to predict properties of sustainable and medicinal materials.
This research theme aims to predict what combinations of cocrystal formers (i.e. templates and reactants) are likely to result in bond-forming reactions in solids. We also aim to predict or explain properties of these solids. We will begin by examining solid state yields of photodimerizations that generate cyclobutene derivatives, ladderanes and other targets which the MacGillivray group has demonstrated can be controlled by modifying functional groups attached to the template and for which an observed ‘yield effect’ remains unexplained. Additional properties to investigate include stereochemical outcome of solid state reactions, electrical conductivity, and solubility. We will work with computational, theoretical, and materials scientists at UdeS, within Canada, and internationally to develop high throughput methods to generate libraries of reactive cocrystals and study structural origins of yields of reactivity. The work will contribute to ongoing efforts of solid-state characterization and lead to new instrumentation for high throughput syntheses and characterization as means to rapidly generate and study large sample sizes. Results of the structural property characterizations will build upon methods in crystal structure prediction and machined learning research.