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The discovery of new crystalline materials is essential to scientific and technological progress.
International Tables for Crystallography
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Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
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Review of computational approaches to predict the thermodynamic stability of inorganic solids
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A universal graph deep learning interatomic potential for the periodic table
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Normalizing flows for atomic solids
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Crystal structure generation with autoregressive large language modeling
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Fine-tuned language models generate stable inorganic materials as text
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Crystal toolkit: A web app framework to improve usability and accessibility of materials science research algorithms
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Scope of machine learning in materials research—a review
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SymmCD: Symmetry-preserving crystal generation with diffusion models
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