Leveraging composition-based energy material descriptors for machine learning models

Abstract

A comparison of several classifiers is presented, with a focus on the key choice and construction of a minimal set of suitable material features. To this end, an investigation is conducted over a properly selected and high quality database reporting low temperature superconductors, featurized by composition-based descriptors. Fully general strategies to reduce the number of descriptors for material classification are proposed and discussed. The first strategy aims at testing possible invariance of the target material property (here the critical temperature) with respect to (binary) groups of composition-based features in the form xiaxjb,a,b∈R. In addition, a multi-objective optimization procedure for reducing the set of composition-based material descriptors is also suggested and tested on the chosen use case. The latter procedure is then proven to be particularly convenient to be used in combination with Bayesian type classifiers. Finally, by means of the best-performing classification models, an analysis is conducted over all the ∼40,000 inorganic compounds without Ni, Fe, Cu, O in Materials Project (and not in the SuperCon database, here used for model training) and the corresponding predictions are provided. Among those, 41 materials are classified to show Tc≥15K with a probability higher than or equal to 0.6.