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.
Related stories
arXiv Machine Learning
2d ago
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models...
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 Machine Learning
Sep 23
Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence
The study benchmarks 12 deep generative crystal structure prediction models against the template-based TCSP 2.0 on 180 test structures, finding that template retrieval achieves the highest top‑1 success (68.3%). Most generative predictions overlap with template substitutions, and removing entire stoichiometric prototype families from training reduces accuracy by 50‑78%, indicating strong prototype dependence. Only a small subset of predictions remain after such removal, suggesting limited genuine de‑novo capability.
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.
arXiv Machine Learning
Sep 10
Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches
The article reviews strategies for modeling chemical disorder in materials, addressing the gap between experimental descriptions of disorder and the detailed configurations required for atomistic simulations and AI workflows. It evaluates traditional approaches such as mean-field theories, cluster expansion, and Monte Carlo, alongside emerging AI-powered methods like universal interatomic potentials and generative models. The review also discusses how AI can accelerate computational schemes and enable disorder-native capabilities, providing a roadmap for integrating disorder into realistic AI-accelerated materials discovery.
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 Machine Learning
1d ago
EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations
EP-Flow introduces a new framework for predicting disordered crystal structures without requiring site-level disorder annotations. It uses an Occupancy Distribution Matrix (ODM) to represent continuous site-by-species occupancies and enforces constraints through a transportation polytope. The method jointly generates occupancies, fractional coordinates, and lattice parameters, achieving state‑of‑the‑art performance on formula‑conditioned disordered CSP benchmarks and recovering chemically meaningful local disorder patterns.
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.