arXiv Machine Learning
Aug 10
DynaCrys: Crystal Generation with Dynamic Space-Group Diffusion
arXiv:2608. 07401v1 Announce Type: cross Abstract: The search for new crystalline materials spans an enormous compositional and structural space.
NextCrystal: a Symmetry-Driven Generative Framework for Crystal Structure Prediction
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.
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arXiv Machine Learning
Jul 31
APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems
arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.
arXiv AI
Jul 9
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.
Hugging Face Trending Papers
Jul 8
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order.
arXiv Machine Learning
Jun 15
XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models
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 AI
Aug 10
ED-CSP: Crystal Structure Prediction from Electron Diffraction
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 Machine Learning
Jun 9
Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
arXiv:2601. 09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge.
arXiv AI
Jul 7
Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
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 Machine Learning
5d ago
Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
arXiv:2608. 13457v1 Announce Type: new Abstract: Generating crystals has recently attracted significant interest due to their broad applications in materials science.
arXiv Machine Learning
Jul 14
CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery
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 Machine Learning
Jun 2
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
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 AI
Jun 9
MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.