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

Weike Ye, Chi Chen, Zhenbin Wang, Iek-Heng Chu & Shyue Ping Ong
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...
2 citations reported since publication in 2019.
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162 downloads reported since publication in 2019.

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