Claims › ext:0148e4ddf0d09501 › line of work

Its line of work

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 learning models and well within the limits of DFT accuracy.

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Claim	Status	Checkable	Credence	Use	Stakes	Rests on
Here we show that deep neural networks utilizing just two descriptors - the Pauling electronegativity and ionic radii -…	○ unchecked	yes	0.55	0	8.1	—

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Where	Status	Claim	Credence
this claim	unchecked	Here we show that deep neural networks utilizing just two descriptors - the Pauling electronegativity and ionic radii - can predict the DFT formation energies…human literature · ext:0148e4ddf0d09501	0.55

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