arXiv (All)
open access
Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conventional metallic alloy nanocrystal catalysts. By accounting for diverse adsorption sites and crystallographic facets, AED
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
- Harnessing Selectivity in CO(2) Hydrogenation: Mechanistic Descriptors, Data-Driven Catalyst Design, and Sustainability Constraints.#1091118
- Active-Space Quantum Simulation of N$_2$ Hydrogenation at a Ru Single-Atom Site on Ru(0001)#1105517
- Work Function and High-Coverage Adsorption Energy as Hydrogen-Evolution Descriptors on Ag-Au-Pd-Pt Alloys#792359
- Work Function and High-Coverage Adsorption Energy as Hydrogen-Evolution Descriptors on Ag-Au-Pd-Pt Alloys#788359
- Frustrated Lewis Pairs (FLPs) for direct CO<sub>2</sub> hydrogenation#1053597
Record · ID 998221
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