arXiv (All)
open access
Benchmarking machine-learning interatomic potentials using classical force fields
We propose a rational comparison between several well-known machine-learning interatomic potentials (linear atomic cluster expansion, the Behler-Parrinello high-dimensional neural network potential, and the graph neural network-based MACE potential) by testing their performance for fitting classical
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
- ECENet: An Edge Cluster Expansion Line-Graph Neural Network#1048880
- A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials#461063
- Fast and Accurate Equivariant Foundation Models for Atomistic Simulation#1048341
- Long-range electrostatics in atomistic machine learning: a physical perspective#617956
- Long-range electrostatics in atomistic machine learning: a physical perspective#617456
Record · ID 1324840
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