Comprehensive Dataset and Machine Learning Analysis for Predicting Adsorption Energies on MXene Surfaces to Enhance Catalyst Design
Published: 23 December 2024| Version 1 | DOI: 10.17632/bct4m7r88s.1
Contributors:
Kais Iben Nassar, Tiago Galvão, Jose Gouveia, José R. B. GomesDescription
This dataset and accompanying analysis provide insights into the adsorption energies of MXene surfaces, leveraging machine learning to enhance catalyst design for the water-gas shift reaction. The repository includes: Comprehensive Dataset on MXene Properties for Catalyst Design (2024): An extensive dataset compiled to characterize MXene properties relevant to catalytic activity, including adsorption energy values and structural parameters. Machine Learning and Data Analysis for MXene-Based Catalysts (Python): A Python-based notebook showcasing machine learning workflows for data analysis, model training, and predictions of adsorption energies.
Files
Institutions
Universidade de Aveiro CICECO
Categories
Catalysis, Machine Learning