Practical feature filter strategy to machine learning for small datasets in chemistry.

ABSTRACT: Many potential use cases for machine learning in chemistry and materials science suffer from small dataset sizes, which demands special care for the model design in order to deliver reliable predictions. Hence, feature selection as the key determinant for dataset design is essential here. We propose a practical and efficient feature filter strategy to determine the best input feature candidates. We illustrate this strategy for the prediction of adsorption energies based on a public dataset and sublimation enthalpies using an in-house training dataset. The input of adsorption energies reduces the feature space from 12 dimensions to two and still delivers accurate results. For the sublimation enthalpies, three input configurations are filtered from 14 possible configurations with different d

SUBMITTER: Hu Y 

PROVIDER: S-EPMC11379859 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature