Materials Cloud Archive
metadata only
CC BY 4.0
data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation
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
- Learning new physics from data: A symmetrized approach#676001
- Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials#660809
- Physics Informed Differentiable Solvers for Learning Parametric Solution Manifolds in Heterogeneous Physical Systems#629904
- Physics Informed Differentiable Solvers for Learning Parametric Solution Manifolds in Heterogeneous Physical Systems#628042
- Scalable photoexcitation-induced molecular dynamics with machine-learned Hamiltonians#662176
Record · ID 206210
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