Authors: J Shivakumara, Ranjana I K
Abstract: Artificial Intelligence (AI) has emerged as a transformative paradigm in materials research, enabling accelerated discovery, precise property prediction, and efficient optimization of materials. Traditional approaches based on empirical experimentation and computational simulations are often constrained by time and cost. AI techniques such as machine learning, deep learning, and generative models overcome these limitations by leveraging large datasets to uncover complex relationships between material structure and properties. This article presents a comprehensive study of AI methodologies, applications, experimental validation, and future directions, supported by data tables and schematic diagrams.
DOI: https://doi.org/10.5281/zenodo.20112744
