Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

Graphene has been regarded as a potential application material in the field of new energy conversion and storage because of its unique two-dimensional structure and excellent physical and chemical properties. However, traditional graphene preparation methods are complicated in-process and difficult to form patterned structures. In recent years, laser-induced graphene (LIG) technology has received a large amount of attention from scholars and has a wide range of applications in supercapacitors, batteries, sensors, air filters, water treatment, etc. In this paper, we summarized a variety of preparation methods for graphene. The effects of laser processing parameters, laser type, precursor materials, and process atmosphere on the properties of the prepared LIG were reviewed. Then, two strategies for large-scale production of LIG were briefly described. We also discussed the wide applications of LIG in the fields of signal sensing, environmental protection, and energy storage. Finally, we briefly outlined the future trends of this research direction.