ScholarlyCommons
metadata only
Predictive Surface Design of Oxide Materials Through First-Principles Thermodynamics and Machine-Learning Sampling
2026
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
- Fine-Tuned Machine-Learning Potential for Accurate Description of Mn$_x$O$_y$H$_z$ Clusters on Cobalt Surfaces#206569
- Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics.#412197
- Explainable machine learning and physics-constrained optimization of chalcogen catalysts for sustainable hydrogen production.#615861
- A robust machine learning framework for predicting contact angle in nano-assisted chemical EOR.#170578
- Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization#925347
Record · ID 929224
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