2 code implementations • 28 Jan 2020 • Christopher J. Bartel, Amalie Trewartha, Qi. Wang, Alex Dunn, Anubhav Jain, Gerbrand Ceder
By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85, 014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids.
Materials Science Computational Physics
