Cameron Ryan

High-pressure phase identification has relied on dynamic graph convolutional networks (DGCNNs) to classify each atom's local environment, which is costly to run. A Gaussian kernel model instead places a set of atom detectors around the origin, sums the isotropic Gaussian density contributed by every atom at each detector, and feeds those values to an ordinary neural network. On test sets with 16, 32 and 64 neighbors it reaches 78%, 86% and 90% accuracy, close to DGCNN's 75%, 85% and 92%, while predicting a full simulation in about 20-30 minutes versus about 5-6 hours for the comparison model on the same HPC environment.

High-pressure phase identification has relied on dynamic graph convolutional networks (DGCNNs) to classify each atom's local environment, which is costly to run. A Gaussian kernel model instead places a set of atom detectors around the origin, sums the isotropic Gaussian density contributed by every atom at each detector, and feeds those values to an ordinary neural network. On test sets with 16, 32 and 64 neighbors it reaches 78%, 86% and 90% accuracy, close to DGCNN's 75%, 85% and 92%, while predicting a full simulation in about 20-30 minutes versus about 5-6 hours for the comparison model on the same HPC environment.