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Neural Networks Predict Crystal Stability

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SAN DIEGO, Sept. 21, 2018 — Researchers at the University of California, San Diego (UCSD) are using neural networks to predict the stability of materials in two classes of crystals: garnets and perovskites. They trained artificial neural networks to predict a crystal’s formation energy using just two inputs: electronegativity and ionic radius of the constituent atoms. Based on this work, they developed models that can identify stable materials in two classes of crystals. According to the team, its models are up to 10× more accurate than previous machine learning models and are fast enough to...Read full article

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