Data for: Machine learning-accelerated simulations enable heuristic-free surface reconstruction

This is the dataset for the publication "Machine learning-accelerated simulations enable heuristic-free surface reconstruction", by X. Du, J.K. Damewood, J.R. Lunger, R. Millan, B. Yildiz, L. Li, and R. Gómez-Bombarelli. The repository contains the density-functional theory (DFT) data used to train the neural network force fields (NFF), selected results from our GaN(0001) and SrTiO₃(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for analysis and plots. To run the .ipynb's, you will need to install surface-sampling and NeuralForceField from the Rafael Gomez-Bombarelli Group @ MIT.