Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction

Abstract

From studying the atomic structure and chemical behavior to the discovery of new materials and investigating properties of existing materials, machine learning (ML) has been employed in realms that are arduous to probe experimentally. While numerous highly accurate models, specifically for property prediction, have been reported in the literature, there has been a lack of a generalized framework. Herein we propose a novel feature selection approach that enables the development of a unified ML model for property prediction for several classes of materials. It involves an ingenious blending of selected features from various classes of data such that the resultant feature set equips the model with global data descriptors capturing both class-specific as well as global traits. We took accurate band gaps of three distinct classes of 2D materials as our target property to develop the proposed feature blending approach. Using Gaussian process regression (GPR) with the blended features, the ML model developed here resulted in an average root-mean-squared error of 0.12 eV for unseen data belonging to any of the participating classes. The feature blending approach proposed here can be extended to additional classes of materials and also to predict other properties.