Machine Learning Approach for the Discovery of Enhanced Magnetocaloric Effect in Single Molecule Magnets

Single molecule magnets (SMM) are candidate materials for magnetocaloric applications, high-density information storage, magnetic qubits, and spintronic devices. These molecules are made of several lanthanide and/or transition metal ions coordinated by organic ligands. Despite the progress made in experimental and traditional first-principles modeling efforts, lack of predictive design guidelines hinder rapid design of SMM for targeted applications. Here, we develop a machine learning approach for predicting novel SMM for magnetocaloric applications. We construct a database from surveying the published literature on magnetocaloric effect in SMM and develop a representation scheme that include aspects related to dimensionality, structure, local coordination environment, ideal number of spins of magnetic ions, ligands, and linking chemistry. We train machine learning models to predict the entropy change. The models capture successfully the observed trends and identify key variables that contribute to the entropy. We also predict new SMMs and await experimental validation.