Machine-Learning-Assisted Materials Discovery from Electronic Band Structure

Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool for pattern recognition has opened exciting opportunities to revolutionize materials discovery. This work explores the application of ML techniques to assist in the discovery of materials using band structure data. The electronic band structure, which describes the energy levels of electrons in a material, holds vital information regarding its electronic and optical properties. The band structure data of 63,588 materials, including metals and insulators, have been retrieved from the Materials Project database. The data were grouped into 85 batches based on the band path in the first Brillouin zone. Three ML clustering algorithms were trained on the band structure data after performing feature selection and engineering, followed by noise reduction. The models were validated by comparing the materials' properties in a cluster.