AI-Driven and Quantum-Informed Design of Functional Nanomaterials: Bridging Catalysis, Drug Discovery, and Sustainable Environmental Remediation
Abstract
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;…
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