Avadhesh Kumar Yadav
ABSTRACT Artificial Intelligence integration in materials science has revolutionized discovery processes, accelerating timelines by 10–100× and identifying over 2.2 million stable inorganic materials through machine learning frameworks. This review examines current AI paradigms—foundation models, quantum machine learning, and autonomous laboratories—while addressing implementation challenges. Four transformative innovations are reshaping materials discovery: graph neural networks achieving >90% accuracy in thermoelectric predictions, generative models enabling inverse design, natural language processing extracting protocols from 100,000+ papers, and autonomous laboratories with 71% synthesis validation success rates. These advances leverage large‐scale databases (Materials Project, NOMAD, OQMD), computational infrastructure, and algorithms spanning supervised learning, reinforcement learning, and deep generative models. Critical case studies include DeepMind's GNoME discovering 380,000 stable materials and Microsoft's MatterGen platform for crystal structure generation. However, persistent challenges remain: data scarcity affects 60% of materials classes, algorithmic bias exists in training datasets, and black‐box limitations restrict scientific insight. Emerging trends—physics‐informed machine learning, multimodal AI, federated learning, and human‐AI collaboration—promise further acceleration. This review provides state‐of‐the‐art assessment and actionable insights for navigating AI‐driven materials discovery transformation, emphasizing the paradigm shift from traditional trial‐and‐error approaches to predictive, autonomous research methodologies that fundamentally reshape scientific investigation and innovation processes.