arXiv (All)
open access
Equivariant Flow Matching for Electron Density Prediction
Machine learning surrogates for density functional theory (DFT) have been increasingly used to reduce the cost of first-principles calculations. In this arena, predicting real-space electron densities offers a scalable and transferable initialization for self-consistent field (SCF) procedures. Howev
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
- Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials#1354320
- Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties#1248391
- Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density#659305
- Accurate and Transferable Intermolecular Potential Based on Machine-Learned Molecular Electron Density#657169
- A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials#461063
Record · ID 1186965
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