This article describes a novel deep learning model, Cond-CDVAE, for crystal structure prediction (CSP), which can generate crystal structures under specific conditions like composition and pressure. This model leverages a vast dataset (MP60-CALYPSO) containing high- and ambient-pressure structures and applies a conditional crystal diffusion variational autoencoder to predict viable material configurations. Cond-CDVAE excels in predicting accurate crystal structures without requiring optimization, showing higher success rates and efficiency compared to traditional CSP methods. This approach addresses the complex energy landscapes of CSP tasks, offering enhanced capabilities for materials discovery, particularly in high-pressure applications. Future improvements are anticipated, including incorporating space-group symmetry and expanding the dataset to enhance the model’s accuracy and versatility in CSP.
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https://www.nature.com/articles/s41524-024-01443-y
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