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Keyword: ionic radius ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Deep neural networks for accurate predictions of crystal stability

   Ye, Chen, Wang, Chu and Ong · Nature Communications · 2018

   The paper shows deep neural networks built on a few chemically intuitive descriptors can predict crystal stability of garnets with low error, and can be extended to mixed garnets using a binary encoding.

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   1. UncheckedDeep neural networks using only electronegativity and ionic radii predicted DFT formation energies of C3A2D3O12 garnets to within 7-8 meV/atom.“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 with extremely low mean absolute errors of 7-8 meV/atom, an order of magnitude improvement over previous machine learni…”
   2. UncheckedA binary encoding scheme may let neural networks handle mixed garnets with little loss in accuracy and only a small rise in descriptor size.“Further extension to mixed garnets with little loss in accuracy can be achieved using a binary encoding scheme that introduces minimal increase in descriptor dimensionality.”