arXiv (All)
open access
An atom-based machine-learned dipole-moment model and application to conjugated systems
We introduce a method for predicting the dipole moments of molecular systems by systematically decomposing the total dipole moment into the sum of atomic contributions using the wavefunction from density-functional-theory calculations, and then use graph neutral networks to predict this effective at
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
- Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation#147031
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.#148193
- Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory#414099
- All-Optical Control of Interfacial Polarization in MoS$_2$/WSe$_2$ Heterobilayers#1109129
- From Dipeptide Systems to Polypeptides: Evolution of Mutual Information.#438975
Record · ID 1143969
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