@Article{cmc.2026.079503,
AUTHOR = {Manjodh Kaur, Princy Randhawa, Jitendra Jaiswal, Deepak Dubal, Ravindra N. Bulakhe, Deepanraj Balakrishnan, Nithesh Naik},
TITLE = {Data-Driven Materials Science Using Machine Learning and Computational Modeling},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26737},
ISSN = {1546-2226},
ABSTRACT = {This review emphasizes the growing role of artificial intelligence (AI) in transforming the materials discovery process into a data-driven and autonomous approach. It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning, generative models, and AI agents are revolutionizing the design, screening, and optimization of materials. A key contribution is a detailed, step-by-step machine learning framework that guides researchers through data collection, preprocessing, feature engineering, model development, and validation, utilizing publicly available materials databases and computational tools. Additionally, the review discusses the latest advances in generative AI and autonomous research systems, highlighting their potential to enable inverse design and closed-loop experiments. It includes a tutorial case study on sodium-ion battery materials to demonstrate practical application in formation energy prediction via machine learning, along with comparisons to high-throughput screening accuracy using density functional theory (DFT). The article also addresses current challenges such as data limitations, model interpretability, and physics-based approaches. Overall, this publication serves as both a conceptual and practical guide for integrating AI into materials research, aiming to accelerate the discovery process and improve efficiency.},
DOI = {10.32604/cmc.2026.079503}
}