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Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

Chemistry

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

E. R. Antoniuk, G. Cheon, et al.

Explore the groundbreaking SynthNN, a deep learning model developed by Evan R. Antoniuk and colleagues, that revolutionizes the prediction of synthesizability in inorganic crystalline materials, outpacing traditional methods and experts alike in speed and accuracy.

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