Search Results for self-driving-laboratories

Abstract

This paper reviews the growing role of AI in accelerating the discovery and design of inorganic nanomaterials for sustainable development through a six-stage pipeline: Data → AI → Prediction → Design → Fabrication → Application. Using a critical-analytical approach supported by quantitative metrics (99.99% correlation for TiO₂ band gap, screening of >10 million oxides, >2 compounds/day at A-Lab), it systematically compares ML, DL, and GNN techniques, alongside DFT-based high-throughput screening, inverse design, active learning, and self-driving laboratories. Based on 30 peer-reviewed references (2013–2026) selected using PRISMA guidelines from Scopus, Web of Science, and Google Scholar.