Computational Design of Chiral Coordination Networks for Advanced Functional Materials
Keywords:
chiral coordination networks, metal-organic frameworks, computational materials design, high-throughput screening, machine learning, reticular chemistry, chirality, infrastructure governanceAbstract
Chiral coordination networks integrate molecular chirality with extended crystalline frameworks, enabling highly selective functions that are essential for enantioselective separations, asymmetric catalysis, and nonlinear optical devices. The computational design of such networks has evolved from heuristic ligand selection to system-level strategies that incorporate high-throughput virtual screening, multi-scale modeling, and machine learning-guided optimization. This paper presents a comprehensive examination of the structural architectures, computational methodologies, infrastructure requirements, and governance frameworks that underpin the rational development of chiral coordination networks. Salient architectural motifs, including helical rod packings, interpenetrated nets, and spontaneous symmetry breaking from achiral precursors, are analyzed in terms of their designability and functional trade-offs. Computational platforms that integrate density functional theory with classical force fields, generative models, and reinforcement learning are discussed as enablers of property prediction across vast chemical spaces. System-level considerations—data pipelines, high-performance computing orchestration, FAIR data management, and sustainability metrics—are addressed as essential components of a translational research ecosystem. Trade-offs between structural robustness, synthetic accessibility, enantioselectivity, and operational stability are examined through multi-objective optimization frameworks. The paper further explores the governance dimensions of computational materials design, including algorithmic fairness, environmental cost of large-scale simulations, intellectual property regimes, and open science policies, culminating in a forward-looking assessment of deployment pathways and collaborative policy infrastructures needed to translate in silico discoveries into functional materials at scale.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
