Electronic Band Structure Prediction with Machine Learning

Within the emerging field of materials informatics, machine learning-based methods are in the forefront of recent research. Although these models usually require a large amount of training data, they can be used for fast and accurate predictions and quantum-mechanical insights. In this work, we explore various machine learning methods to predict an electronic band structure bypassing computationally demanding ab initio calculations. We investigate the problem on a vast amount of randomly generated effective tight-binding band structures. We also estimate the applicability of the developed methods to real materials contained within the Organic Materials Database (OMDB) [http://omdb.diracmaterials.org] using different crystal structure representations. Finally, we briefly discuss the inverse problem of prediction of the of the impact to the initial crystal structure given a slight modification of the corresponding electronic structure.