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Interpretable and Explainable Machine Learning for Materials Science and Chemistry | Accounts of Materials Research
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Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics | MRS Communications | Cambridge Core
Machine Learning-Aided Materials Design Platform for Predicting the Mechanical Properties of Na-Ion Solid-State Electrolytes | ACS Applied Energy Materials
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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks | npj Computational Materials
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