The results of the sixth blind test of organic crystal structure prediction methods are presented and discussed, highlighting progress for salts, hydrates and bulky flexible molecules, as well as on-going challenges.

Previously, it was shown that crystal structure prediction based on genetic algorithms (MGAC program) coupled with force field methods could consistently find experimental structures of crystals. However, inaccuracies in the force field potentials often resulted in poor energetic ranking of the experimental structure, limiting the usefulness of the method. In this work, dispersion-corrected density functional theory is employed to improve the accuracy of the energy rankings, using the software package Quantum Espresso. The best choices of running parameters for this application were determined, followed by completion of crystal optimizations on a test set of archetypical pharmaceutical molecules. It is shown here that the variable cell optimization of experimental structures reproduces the experimental structure with high accuracy (RMS < 0.5 Å) for this test set. It is also shown that the use of electronic structure theory based methods greatly improves the energetic ranking of structures produced by MGAC when used with a force field method, such that the experimental match is found with a high degree of accuracy.

Here we present the results of our unbiased searches of glycine polymorphs obtained using the Genetic Algorithms search implemented in Modified Genetic Algorithm for Crystals coupled with the local optimization and energy evaluation provided by Quantum Espresso. We demonstrate that it is possible to predict the crystal structures of a biomedical molecule using solely first principles calculations. We were able to find all the ambient pressure stable glycine polymorphs, which are found in the same energetic ordering as observed experimentally and the agreement between the experimental and predicted structures is of such accuracy that the two are visually almost indistinguishable.

Human exposure to particulate matter and other environmental species is difficult to estimate in large populations. Individuals can encounter significant and acute variations in exposure over small spatiotemporal scales, and exposure is strongly tied to both the environmental and activity contexts that individuals experience. Here we present the development of an agent based model to simulate human exposure at high spatiotemporal resolutions. The model is based on simulated activity and location trajectories on a per-person basis for large geographical areas. We demonstrate that the model can successfully estimate trajectories and activity patterns that have been validated against traffic patterns and that can be integrated with exposure-agent geographical distributions to estimate total human exposure.