Computational Science Initiative Event

Abstract: Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks of interest. In this talk, we present a novel AFE scheme that effectively extracts relationships from data that can be interpreted based on functional formulas to discover their physical meaning or new hypotheses. Here we focus on materials science applications, where interpretable predictive modeling may enhance our understanding of materials systems and also guide the discovery of new materials. Typically, it is computationally prohibitive to exhaustively explore all potential relationships to identify interpretable and predictive features. We overcome this challenge by designing an AFE strategy that efficiently explores a feature generation tree (FGT) using a deep Q-network (DQN) for scalable and efficient exploration of the feature space in an automated manner. The proposed DQN-based AFE strategy shows promising results when benchmarked against existing AFE methods based on several materials science datasets.