Machine Learning-Assisted Design of Chiral Supramolecular Materials for Selective Molecular Recognition and Sensing
Keywords:
machine learning, chiral supramolecular materials, molecular recognition, sensing, materials informatics, data governance, socio-technical systemsAbstract
The rational design of chiral supramolecular materials for selective molecular recognition and sensing requires simultaneous control over molecular geometry, noncovalent interaction networks, solvent response, and chiroptical readout. Traditional empirical and computational screening strategies are often constrained by the combinatorial size of the chemical space and by the difficulty of predicting emergent supramolecular chirality from monomer-level descriptors. This paper examines machine learning-assisted design not as an isolated predictive tool but as a systems problem spanning data infrastructure, model architecture, experimental validation, deployment, governance, and institutional policy. A system-level perspective is developed to address structural trade-offs between descriptor fidelity and computational tractability, between model expressiveness and interpretability, and between laboratory optimization and field-level sensing robustness. The discussion integrates concepts from molecular representation learning, high-throughput virtual screening, supramolecular analytical chemistry, and materials informatics. It further considers fairness and accountability in data-driven materials workflows, the sustainability of computational and experimental cycles, and the policy implications of autonomous discovery platforms. The paper argues that selective recognition and sensing in chiral supramolecular systems will require not only improved predictive accuracy but also coherent socio-technical architectures that connect machine learning outputs to experimental logic, regulatory expectations, and long-term institutional memory. A forward-looking framework is proposed for coupling generative molecular design, chirality-sensitive validation, and adaptive sensor deployment in a manner that is robust, interpretable, and socially accountable.
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