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Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges.
Self-consistent equations including exchange and correlation effects
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A general-purpose machine learning framework for predicting properties of inorganic materials
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
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3-d inorganic crystal structure generation and property prediction via representation learning
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Neural ordinary differential equations on manifolds
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