Work

Modular Ontology-Driven Data Capturing for Machine Learning-Assisted Supported Catalytically Active Metal Solutions Design

Year: 2025

Type: article

Abstract: Employing the modular ontology modelling approach to capture experimental data from various researchers. The data is used for machine learning training for SCLAMS catalyst discovery.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17409408

Authors: Stangl, Philipp, Haumann, Marco, Lenz, Richard

Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7093083460

FWCI:

Cites:

Cited by:

Related to: