arXiv (All)
open access
Reference-free certification of machine-learning interatomic potentials
Universal machine-learning interatomic potentials now reach held-out energy and force errors so small that they no longer predict how a model behaves in simulation. Here we show that a potential can be graded without any reference calculation, against properties the exact Born-Oppenheimer surface sa
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
- Long-range electrostatics in atomistic machine learning: a physical perspective#617956
- Long-range electrostatics in atomistic machine learning: a physical perspective#617456
- Certifying Learned Variables#713973
- Machine learning for molecular and materials science#686317
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought#461065
Record · ID 1155520
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