Abstract Recent advances in artificial intelligence (AI) offer significant opportunities to drive industrial transformation by addressing growing societal demands for products, techno-economic efficiency, and reduced carbon footprints. This review presents a structured framework for building transparent, scalable, and sustainable AI-driven infrastructures spanning conceptualization to commercialization for materials discovery and advanced manufacturing. The framework traces the evolution of materials development from empirical approaches toward integrated AI-enabled platforms, emphasizing open-source tools that unify data acquisition, modeling, simulation, and deployment to democratize access, foster collaboration, and enhance reproducibility. Key enabling components include self-driving laboratories for real-time optimization, advanced computational approaches for high-fidelity data, and blockchain-based mechanisms for secure data sharing, provenance, and supply-chain traceability. The review further discusses the importance of machine learning for materials property prediction, synthesis and process optimization, together with scalable cloud–edge architectures that improve efficiency and reduce latency. Emphasis is placed on lifecycle-aware design, techno-economic analysis, and ethical AI principles to align industrial development with global sustainability goals.
Salas et al. (Tue,) studied this question.