arXiv (All)
open access
Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants
We present a machine learning (ML) method to determine unit cell parameters from powder X-Ray diffraction (XRD) data using a novel invariant lattice representation. In ML, the data representation used can have a substantial impact on the prediction quality. Previous approaches have directly predicte
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
- uFlowCSP: Crystal Structure Prediction using Mean flow generative models#996619
- Tackling real-world crystal structure prediction from powder X-ray diffraction data.#455224
- Scalable machine learning framework for multiphase identification from powder X-ray diffraction#974987
- XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction#1218363
- Data-driven prediction of thin-film metallic glass forming ability via Bayesian classification and experimental verification in the Cu-Zr-Al system.#796645
Record · ID 1215434
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