Discovery of Novel Materials Through Machine Learning.

Abstract

Experimental exploration of new materials relies heavily on a laborious trial-and-error approach. In addition to substantial time and resource requirements, traditional experiments and computational modelling are typically limited in finding target materials within the enormous chemical space. Therefore, creating innovative techniques to expedite material discovery becomes essential. Recently, machine learning has emerged as a valuable tool for material discovery, garnering significant attention due to its remarkable advancements in prediction accuracy and time efficiency. This rapidly developing computational technique accelerates the search and optimization process and enables the prediction of material properties at a minimal computational cost, thereby facilitating the discovery of novel materials. We provide a comprehensive overview of recent studies on discovering new materials by predicting materials and their properties using machine learning techniques. Beginning with an introduction of the fundamental principles of machine learning methods, we subsequently examine the current research landscape on the applications of machine learning in predicting material properties that lead to the discovery of novel materials. Finally, we discuss challenges in employing machine learning within materials science, propose potential solutions, and outline future research directions.