Energy and Sustainability

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Browsing Energy and Sustainability by Subject "'adsorption energy predictions'"

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    Input dataset used for the ML prediction of C, H, O, and S adsorption energies
    (Cranfield University, 2023-11-06 17:09) Wang Wang, Siqi
    The database used for the ML model training consists of DFT-calculated adsorption energies of C, H, O, and S on 23 monometallic and 12 bimetallic surfaces. Each pure metal is represented by a set of 12 features, including fundamental properties (e.g. group, atomic number, covalent radius, etc) and surface-related properties (e.g. surface free energy, work function, etc). Each alloy (M1xM2y) is represented by the features of its individual components (12 features of M1 plus 12 features of M2) and the ratio of x:y to account for the concentration of each component within the binary system. For monometallic inputs, the ratio was considered as 1. The adsorbates (C, H, O, and S) are represented by a set of 9 properties, including group, atomic number, first ionization potential, etc.
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    ItemOpen Access
    Predicted C, O, H, and S adsorption energies on bimetallic surfaces (with a M1:M2 ratio of 3)
    (Cranfield University, 2023-11-02 10:27) Wang, An
    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).