DOI: https://doi.org/10.36347/sjpms.2025.v12i09.002

Pages: 389-418

The convergence of machine intelligence, quantum-accurate simulation, and laboratory automation is reshaping how functional nanomaterials are conceived, validated, and deployed across chemistry, medicine, and environmental engineering. This review synthesizes an end-to-end “data-to-device” framework for the AI-driven and quantum-informed design of nanomaterials that bridges three application pillars: (i) green catalysis for clean energy and circular chemistry, (ii) drug discovery and nano-enabled therapeutics, and (iii) sustainable environmental remediation. We survey inverse-design workflows that combine generative models, uncertainty-aware predictors, Bayesian optimization, and active learning with electronic-structure engines (DFT, GW/BSE), free-energy methods (FEP/TI), and machine-learned interatomic potentials to span accuracy–throughput trade-offs via multi-fidelity strategies. On the materials side, we map tunable design spaces single-atom catalysts, 2D/defect-engineered surfaces, porous frameworks (MOFs/COFs), quantum dots, membranes, and bio-hybrids linking structure, defects, and interfacial physics to catalytic turnover, molecular recognition, transport, and durability. For catalysis, we outline pipelines that couple adsorption-energy maps and microkinetics to target CO₂ reduction, OER/ORR, and selective oxidations; for therapeutics, we integrate target modeling, generative ideation, physics-based ΔG estimation, and ADMET triage with synthesis-aware constraints; for remediation, we align pollutant fingerprints with adsorption, photocatalysis, electrocatalysis, and membrane routes while tracking leaching and secondary byproducts. Throughout, we emphasize rigorous reporting reproducible data splits, calibrated uncertainty, and minimum information for models and experiments together with life-cycle assessment, techno-economic analysis, and green-chemistry metrics (e.g., PMI, E-factor) to ensure net-positive impact. We close with a roadmap for closed-loop, se