Data from: Deep neural networks for accurate predictions of crystal stability

Predicting the stability of crystals is one of the central problems in
materials science. Today, density functional theory (DFT) calculations
remain comparatively expensive and scale poorly with system size. Here we
show that deep neural networks utilizing just two descriptors—the Pauling
electronegativity and ionic radii—can predict the DFT formation energies
of C3A2D3O12 garnets and ABO3 perovskites with low mean absolute errors
(MAEs) of 7–10 meV atom−1 and 20–34 meV atom−1, respectively, well within
the limits of DFT accuracy. Further extension to mixed garnets and
perovskites with little loss in accuracy can be achieved using a binary
encoding scheme, addressing a critical gap in the extension of
machine-learning models from fixed stoichiometry crystals to infinite
universe of mixed-species crystals. Finally, we demonstrate the potential
of these models to rapidly transverse vast chemical spaces to accurately
identify stable compositions, accelerating the discovery of novel
materials with potentially superior properties.