Machine-Learning Thermal Properties

Most computational materials databases are currently limited to properties calculated at zero temperature, but the inclusion of temperature can vastly change the energetic landscape, influencing our predictions of what compounds we believe to be stable and synthesizable. However, the high cost of computing temperature-dependent properties prohibits large high-throughput studies from being performed. Here, we train a simple machine-learning model to efficiently predict the vibrational entropy and free energy of materials from composition alone. Other previous studies include similar models trained on small datasets of hundreds of compounds at only a single temperature, but our model was trained on a set of thousands of compounds and achieves better accuracies over a broad range of compositions, temperatures, and structural complexities. The accuracy and low computational cost of this approach make it possible to generate temperature-dependent phase diagrams for numerous systems, providing insight into the effect of temperature on stability.