Predicted C, O, H, and S adsorption energies on bimetallic surfaces (with a M1:M2 ratio of 3)

dc.contributor.author	Wang Wang, Siqi
dc.date.accessioned	2024-05-27T06:25:12Z
dc.date.available	2024-05-27T06:25:12Z
dc.date.issued	2023-11-02 10:27
dc.description.abstract	The best-performing ML model was then applied to a list of bimetallic alloys, the adsorption energies of which were not readily available. A total of 24 metal elements were considered and permuted with one another, which generated a set of over 500 bimetallic alloys. One of the input features used for the ML model is the ratio of the two individual components within the binary system. By changing the numerical value of the “ratio” feature, the ML model is able to deal with a given binary alloy with any M1 or M2 concentration. In this work, we focused on bimetallic materials with a M1:M2 ratio of 3 (i.e. 75 mol.% of M1 and 25 mol.% of M2).
dc.identifier.citation	Wang, Siqi Wang (2023). Predicted C, O, H, and S adsorption energies on bimetallic surfaces (with a M1:M2 ratio of 3). Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.24486466
dc.identifier.doi	10.17862/cranfield.rd.24486466
dc.identifier.uri	https://dspace.lib.cranfield.ac.uk/handle/1826/21722
dc.publisher	Cranfield University
dc.rights	CC BY 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	'adsorption energy predictions'
dc.subject	'bimetallic alloy surfaces'
dc.title	Predicted C, O, H, and S adsorption energies on bimetallic surfaces (with a M1:M2 ratio of 3)
dc.type	Dataset