AI-Driven Molecular Simulation for Predicting Metal-Ion-Regulated Supramolecular Chirality in Functional Materials

Abstract

Supramolecular chirality in functional materials is increasingly controlled by metal-ion coordination, yet predicting the emergent handedness, chiroptical response, and stability of these assemblies remains a formidable challenge. Conventional quantum chemical and classical simulation methods provide physical insight but impose severe computational and sampling limitations when exploring the vast configurational and compositional space of metal-organic and coordination-driven systems. Artificial intelligence, especially machine-learned potentials, generative models, and active learning workflows, offers a route toward predictive molecular simulation at scale. This paper presents a system-level analysis of AI-driven molecular simulation platforms for metal-ion-regulated supramolecular chirality. Rather than treating prediction as an isolated algorithmic task, the analysis emphasizes multiscale data infrastructure, modular simulation architectures, representation learning for chirality, uncertainty quantification, robustness, governance, and deployment constraints. The study discusses how coordination geometry, ligand chirality, solvent effects, and kinetic trapping interact across scales and how AI architectures can be organized to capture these interactions without sacrificing interpretability or experimental relevance. Structural trade-offs between accuracy, latency, data efficiency, and generalizability are examined. The paper further addresses fairness, reproducibility, dual-use considerations, and sustainability in autonomous materials discovery. It concludes that durable progress requires integrating AI surrogates with experimental feedback, standardized metadata, and policy-aware infrastructure. The resulting framework supports the rational design of chiral functional materials for sensing, catalysis, separation, and optoelectronics.