Data for: Machine learning-accelerated simulations enable heuristic-free surface reconstruction
收藏资源简介：
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 SrTiO<sub>3</sub>(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for analysis and plots. To run the <code>.ipynb</code>'s, you will need to install surface-sampling (tested up to commit <code>41f192d43bb5a448f5360f9144e6b980e36f88c1</code> on <code>master</code>) and NeuralForceField (tested up to commit <code>7f528f4b89c96c92d264ada413c094cf6972ce8b</code> on <code>master</code>) from the Rafael Gómez-Bombarelli Group @ MIT.
