Magnetic MXene: A Machine-Learning Model With Small Data

Abstract

MXenes, comprising of atomically thin layers of transition metal nitrides, carbides, and carbonitrides, exhibit properties which are not found in their corresponding bulk materials. Interestingly, because of the presence of transition metal, MXenes may also provide the candidate materials for observing low-dimensional magnetism. This can be of interest to various applications such as data storage, electromagnetic interference shielding, and spintronic devices. Here, we focus on the magnetic MXenes, which are only a few in number out of known MXenes. We propose machine learning models to predict the magnetic moments of the MXenes and to classify the MXenes based on their chemical stability. Using these models, we propose four new chemically stable MXene materials having a potentially high magnetic moment.