arXiv (All)
open access
Truncated automatic sparse differentiation for machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynamics and are readily obtained using automatic differentiation. Higher-order derivatives, most notably the Hessian, descri
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
- Truncated automatic sparse differentiation for machine learning interatomic potentials#978436
- Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.#449872
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.#25642
- Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics#1115545
- The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning#1117790
Record · ID 1034326
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