This article discusses a machine learning approach using Long Short-Term Memory (LSTM) networks to predict point defect percentages in semiconductor materials based on simulated X-ray diffraction (XRD) data. The model, trained on silicon data, achieves low prediction errors for various semiconductors, including AlAs and ZnS. Key innovations include a sliding window technique to extract temporal features and the model’s ability to generalize across materials with different crystal structures. The results highlight the effectiveness of this method in accurately predicting material defects.
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https://www.nature.com/articles/s41598-024-75783-6
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