Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution.

Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate a large dataset of Monte Carlo simulations of abnormal grain growth. We train simple graph convolution networks to predict which initial microstructures will exhibit abnormal grain growth, and compare the results to a standard computer vision approach for the same task.

Implementing a Pharmacist-Led Primary Care Pharmacogenomics Medication Management Service.

: Pharmacogenomics (PGx) is a tool to guide optimal medication selection. Increased demand for personalized medicine and the growing occurrence of chronic diseases are drivers for pharmacogenomic medication management services. A review of implementation models identified a paucity of models delivering these services utilizing pharmacists in primary care.