Recent trends in computational tools and data-driven modeling for advanced materials

Abstract

The paradigm of advanced materials has grown exponentially over the last decade, with their new dimensions including digital design, dynamics, and functions. Materials modeling such as that of their properties and behavior in various environments using ab initio approaches, force-field methods and machine learning represents a key step in advanced research. Computational techniques and theoretical models pave the way for establishing the structure–property relationship for designing advanced materials with novel properties and improving their performances. Likewise, high accuracy and fewer computational resources of machine-learning approaches have been widely considered for materials design in the recent years. Furthermore, the information derived from materials studies needs to be properly stored and re-analyzed, making big data analysis an essential requirement for further investigations. The information thus generated has also led to the evolution of the genome of materials for the fostering of advanced materials. Thus, various theoretical and computational approaches provide useful predictions about materials properties and efficiency, ultimately leading to the substantial improvements for new-age devices.