  Crystal structure search is a long-standing challenge in materials design. We
present a dataset of more than 100,000 structural relaxations of potential
battery anode materials from randomized structures using density functional
theory calculations. We illustrate the usage of the dataset by training graph
neural networks to predict structural relaxations from randomly generated
structures. Our models directly predict stresses in addition to forces, which
allows them to accurately simulate relaxations of both ionic positions and
lattice vectors. We show that models trained on the molecular dynamics
simulations fail to simulate relaxations from random structures, while training
on our data leads to up to two orders of magnitude decrease in error for the
same task. Our model is able to find an experimentally verified structure of a
stoichiometry held out from training. We find that randomly perturbing atomic
positions during training improves both the accuracy and out of domain
generalization of the models.
