no code implementations • 7 Dec 2020 • Amalie Trewartha, John Dagdelen, Haoyan Huo, Kevin Cruse, Zheren Wang, Tanjin He, Akshay Subramanian, Yuxing Fei, Benjamin Justus, Kristin Persson, Gerbrand Ceder
This has created a challenge to traditional methods of engagement with the research literature; the volume of new research is far beyond the ability of any human to read, and the urgency of response has lead to an increasingly prominent role for pre-print servers and a diffusion of relevant research across sources.
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
