Applied Artificial Intelligence in Materials Science and Material Design

Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover and develop new materials with desired properties. However, these processes can be time-consuming, resource-intensive, and often limited by the complexity of material systems. The advent of artificial intelligence (AI), particularly machine learning, has revolutionized materials science by offering powerful tools to accelerate the discovery, design, and characterization of novel materials. AI not only enhances the predictive modeling of material properties but also streamlines data analysis in techniques like X-Ray diffraction, Raman spectroscopy, scanning probe microscopy, and electron microscopy. By leveraging large datasets, AI algorithms can identify patterns, reduce noise, and predict material behavior with unprecedented accuracy. In this review, recent advancements in AI applications across various domains of materials science, including spectroscopy, synchrotron studies, scanning probe and electron microscopies, metamaterials, atomistic modeling, molecular design, and drug discovery, are highlighted. It is discussed how AI-driven methods are reshaping the field, making material discovery more efficient, and paving the way for breakthroughs in material design and real-time experimental analysis.