arXiv (All)
open access
Application of Machine Learning for the Identification of 2D Colloidal Assemblies: A Case Study on Particles of Distinct Shapes
This work addresses the problem of identifying colloidal monolayer assemblies using particles of various shapes (two-dimensional coatings): spheres, ellipsoids, cuboids, and rods. The following classification of assemblies is considered: isolated particles, dimers, chains, clusters, and loops. The Y
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
- Comparing dimensionality reduction methods for local structural identification in colloidal systems#998011
- Flow-Induced Intermittent Transport Shapes Colloid Filtration in Complex Media#1120549
- Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning#925063
- Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning#920829
- Image-Guided Pavement Defect Recognition in GPR Data with novel 3D Deep Learning Architecture#658900
Record · ID 986279
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