The prediction of single-molecule magnet properties via deep learning

Abstract

This work involves extraction of salen metal complexes from the Cambridge Structural Database for deep learning to examine the 3D structural features that allow such complexes to act as single-molecule magnets. This research attempts to link a crystal structure database as big data with the molecular design of nanomaterials using artificial intelligence. The approach pioneers the future secondary use of similar crystal structure data.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.