Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis.

ABSTRACT: The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for th

SUBMITTER: Taniike T 

PROVIDER: S-EPMC10786848 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature