Article

Predicting the Synthesizability of Crystalline Inorganic Materials from the Data of Known Material Compositions

2023-03-17

Abstract excerpt

<title>Abstract</title> <p>Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model (<italic>SynthNN</italic>) that leverages the entire corpus of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classificati...

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Literature Corpus work: 268513e0-cf83-52f5-a113-4988b0a78ba3
DOI: 10.21203/rs.3.rs-2574875/v1

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