Ioannis Theocharis, Panagiotis Krokidas, Vassilis Gkatsis, George Giannakopoulos
Abstract
The development of materials with tailored properties is often viewed as the holy grail of chemistry, due to the rather complex structure-property correlations. Artificial Intelligence (AI) and Machine Learning (ML) predictive models have been introduced in the field, as means of unveiling intricate said correlations. However, the application of these models in the physical sciences is hindered by the high cost and substantial time required to generate the necessary data for training AI algorithms. Given this backdrop of data scarcity, methods that efficiently sample and explore an unknown material’s design space can significantly reduce laboratory costs and time.