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Predicting New Materials for Magnetism: Employing Data Science with Machine Learning

Novel magnetic materials have demonstrated a myriad of applications ranging from data storage to security needs. Systematic materials-by-design methods that aim to predict novel magnetic materials are starting to emerge but this presents a serious challenge. This PhD project aims to tackle this materials discovery problem via data science. By exploiting database auto-generation code, that has been developed by the Molecular Engineering (MolE) group at the Cavendish Laboratory, the world’s repository of magnetic property data will be first captured from the literature and raw data depositories, and stowed in an auto-generated magnetism database. Machine learning tools (neural networks, genetic algorithms) will then be employed in order to determine data patterns that represent previously unobserved trends between chemical structure and magnetic properties. Accordingly, a series of material predictions will be generated and short-listed via statistical methods. The student will have the opportunity to test their material predictions via experimentalists in the MolE group, in collaboration with the Rutherford Appleton Laboratory.