arXiv (All)
open access
uFlowCSP: Crystal Structure Prediction using Mean flow generative models
Crystal structure prediction (CSP) is fundamental to computational materials discovery. Generative models including CDVAE, DiffCSP, FlowMM, and CrystalFlow learn stable-crystal distributions directly, but diffusion and flow-matching inference requires tens to thousands of sequential network evaluati
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence#1062393
- Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence#1043101
- EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations#1156587
- Topology-Stratified Materials Discovery with A Flow-Based Generative Model#1062436
- Topology-Stratified Materials Discovery with A Flow-Based Generative Model#1043136
Record · ID 996619
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive