arXiv:2608. 07401v1 Announce Type: cross Abstract: The search for new crystalline materials spans an enormous compositional and structural space.
arXiv:2608. 13457v1 Announce Type: new Abstract: Generating crystals has recently attracted significant interest due to their broad applications in materials science.
arXiv:2602. 17176v4 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding.
arXiv:2604. 13354v2 Announce Type: replace-cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science.
arXiv:2606. 14003v1 Announce Type: cross Abstract: Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science.
arXiv:2509. 15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design.
arXiv:2604. 02270v2 Announce Type: replace-cross Abstract: Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample.
arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.
arXiv:2608. 06448v1 Announce Type: cross Abstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem.
arXiv:2502. 02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science.
arXiv:2607. 11712v1 Announce Type: new Abstract: Inverse design is an emerging data-driven paradigm for efficiently navigating vast chemical spaces to discover new materials with targeted properties, and in the context of heterogeneous catalysis, surface generative models have recently advanced this goal by directly generating catalyst surface-adsorbate structures.
arXiv:2410. 08562v5 Announce Type: replace-cross Abstract: Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics.
arXiv:2606. 00666v1 Announce Type: cross Abstract: Transition metal complexes are central to catalysis, drug design, and materials science, with relevant properties strongly sensitive to their three-dimensional geometry.