REVOLUTIONIZING MATERIAL SCIENCE WITH AI: FROM PREDICTIVE MODELLING TO INNOVATIVE DISCOVERY

In recent years, the field of material science has undergone a significant transformation, increasingly
incorporating artificial intelligence (AI) techniques to accelerate the materials discovery process. This
integration has revolutionized the approach to material development, shifting the focus towards data-driven
methods. The application of AI in this field primarily revolves around two key methodologies: forward
modelling for predictive analysis and inverse modelling for optimization and design. Forward modelling
leverages AI to predict and simulate material properties and behaviours, thereby facilitating the
identification of materials with specific characteristics desired for mechanical applications. Inverse
modelling, on the other hand, utilizes AI for the optimization and design of materials, enabling more
efficient exploration of the vast material space and the creation of materials with optimal properties tailored
for specific applications. This review article provides a comprehensive overview of the evolution and
current state of AI applications in material science. It highlights key techniques, discusses future directions,
and examines the challenges and ethical considerations inherent in this rapidly advancing field. The
integration of AI in material science not only enhances the efficiency and accuracy of material discovery
and design but also opens new avenues for innovation in mechanical engineering and related disciplines.