ISTA Publications
metadata only
How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons
We investigate the locality of magnetic response in polycyclic aromatic molecules using a novel deep-learning approach. Our method employs graph neural networks (GNNs) with a graph-of-rings representation to predict nucleus independent chemical shifts (NICS) in the space around the molecule. We trai
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
- From the Loss Landscape to Diverse Feature Learning in Neural Networks#793177
- From the Loss Landscape to Diverse Feature Learning in Neural Networks#789173
- Building Transformation Layers for Riemannian Neural Networks#1131694
- Geometric Deep Learning for Topologically Consistent Modeling of Cortical Anatomy / Fabian Nico Alexander Bongratz ; Gutachter: Hervé Lombaert, Daniel Rückert, Christian Wachinger ; Betreuer: Christian Wachinger#1065203
- A hybrid deep-learning-architecture for identifying cotton content in fabric materials.#157874
Record · ID 1039236
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