Dataset for the preparation and application of multi-component electrocatalysts in methanol oxidation based on non-precious metals for fuel cell and sensor under ambient-conditions

Artificial intelligence methods facilitate data exploration, application, and analysis, increasing significantly, including machine learning. Data must be reliable and accessible, which places high demands on its acquisition and storage. This complexity is partly due to the wide range of lengths and time scales involved in the many different processes. The data in this article refer to the materials prepared as nonprecious metal for methanol oxidation in fuel cells and sensors. Metal oxides are an alternative for such applications. Important parameters can be used to assess the performance of electrocatalysts: These four parameters are related to other quantities available as a dataset, namely castelli perovskite. The Castelli perovskite dataset contains data conduction band energy level value, heat of formation in eV, Fermi energy level, Fermi bandwidth, material, chemical formula, electronic band gap, magnetic moment, crystal structure, valence band energy level value). The Castelli perovskite dataset is then connected to experimental data through the material's chemical formula. Nonprecious metal oxides include 483 materials (electrocatalyst materials). Based on the results of KMeans data processing, 483 electrocatalyst materials were grouped into 4 types. Thus, non-noble metal oxides can be categorized into 4 types. Correlation data processing shows that the current density correlates with the proposed electrocatalyst type.