arXiv Machine Learning
Jun 2
Latent Diffusion Pretraining for Crystal Property Prediction
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
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.
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arXiv Machine Learning
Aug 3
Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation
arXiv:2607. 28776v1 Announce Type: new Abstract: Generative machine learning is increasingly used for inorganic crystal structure generation.
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 AI
Jun 3
Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation
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 AI
Jul 29
Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
arXiv Machine Learning
Jun 3
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
arXiv:2502. 02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science.
arXiv Machine Learning
Jul 15
SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems
arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.
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.
arXiv Machine Learning
Jun 17
Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictors
arXiv:2410. 08562v5 Announce Type: replace-cross Abstract: Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics.
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 AI
Jun 30
Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop
arXiv:2606. 29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science.
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.