Insights on materials space

Using a kernel-based machine learning surrogate model, we present insights on generating and choosing the training and testing data for optimal modeling of materials space. We introduce a tool that helps us build an “ideal” kernel, which predicts with high accuracy on small training sets. We also present a methodology for quantifying the accuracy of any kernel based surrogate model for interpolating materials space. Our insights (based on analyzing data from over 73,000 unrelaxed DFT calculations comprising 45 different materials) helped improve our model’s predictions by as much as 50% for some systems.