(376ak) Building Computational Tools to Help Guide Experimentalists in the Discovery of New Solid Phases of Organic Materials

Due to the fact that crystalline structures of small organic molecules can vary in properties, fields such as as pharmaceutical formulation, organic electronics processing and explosives preservation have interest in fast and efficient methods to predict favorable structures. A failure to know all potential solid forms has lead to recalls, reformulation, or a missed opportunity to utilize a material to its fullest extent.

Common computational methods are great at predicting new structures, but fail to accurately rank the stability of predicted structures by neglecting temperature and entropic effects. Structure predictions have relied on lattice energy to determine the crystalline stability despite numerous experimental and computational studies that have shown that enantiotropic behavior is prevalent. In particular, one study has shown that 20% of a set of > 500 experimentally know polymorphs re-rank with temperature.

My studies have been focused on improving and testing computational techniques to efficiently determine the relative free energy stability of organic polymorphs. To date, I have looked at how the following factors affect polymorph stability for our methods: 1) the effects of thermal expansion, 2) the importance of anharmonic motions, 3) force field complexity, and 4) force field sensitivity. All tests have the end goal of making our methods faster and more accurate to eventually integrate the use of free energy as the criterion for stability in predicting new crystalline structures. Additionally, all techniques are available to use by those interested in two python based code packages that I manage.