This invention discloses a
machine learning-based method for screening heat-resistant
sintering supported
metal catalysts, involving the interdisciplinary field of catalyst development and
machine learning. The specific steps of this method are as follows: Using the interface
system of
metal nanoparticles and
oxide supports as the screening object, and the interface adhesion energy as the prediction target, a
wetting contact angle close to 90° is set as the criterion for
heat resistance to
sintering; first, a standardized dataset is constructed, then two types of interface descriptors are constructed as input features, and a prediction model is trained using the SISSO
algorithm to predict the interface adhesion energy. Finally, the
contact angle is calculated and ranked accordingly, outputting candidate material combinations. This invention sets evaluation criteria, constructs a standardized dataset, and innovatively designs element-level interface descriptors to achieve precise and scientific catalyst screening; it uses an
adaptation algorithm to
train the model, combines an association
algorithm to transform parameters, and designs
ranking rules to achieve efficient screening and performance quantification, combining
machine learning and interface science to provide
technical support for catalyst development.