arXiv (All)
open access
SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion
Reliable crystal structure prediction (CSP) at finite temperatures, including quantum anharmonic effects, remains challenging but is particularly important for systems containing light atoms such as superconducting hydrides. Here, we integrate machine-learned interatomic potentials (MLIPs) with the
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
- General, Data-Driven Representations and Algorithms for Catalyst Design and Discovery#634804
- Deep Learning-Based Superconductivity Prediction and Experimental Tests#372563
- Predicting the Stepwise Growth of Lithium Clusters Around Sulfur Species (S and S 2 ) From the Topology of the Electron Localization Function#677126
- Doping-controlled topological superconducting transition in misfit layer compounds#984460
- Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites#20406
Record · ID 987543
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