Machine Learning for Next‐Generation Functional Materials

Abstract

Machine learning (ML) is a powerful technique for extracting insights from multivariate data quickly and efficiently. It provides a much-needed way to speed up the research and investigation of functional materials in order to address time-sensitive worldwide issues like COVID-19. But Scientists on the other hand are reporting and patenting new functional materials on a day-to-day basis covering medicine to aerospace. The challenge for researchers is to choose the ideal materials for the design and fabrication of devices and instruments to withstand all weather conditions. Machine learning has been developed for a variety of applications in recent years, including diverse experimentation, device optimization, and material discovery. Increased functionality in next-generation functional materials can improve the application, productivity, and energy efficiency and these qualities can also be used to create a new design concept for renewable energy generation. There is a thorough introduction to the principles of machine learning for functional materials. This chapter covers the challenges in advanced functional materials research and the role of machine learning in design, simulation, and evaluation. Finally, significant pointers to successful machine learning applications are addressed, as well as the remaining hurdles in machine learning for next-generation functional materials.