Computational Screening of Zeolitic Imidazolate Frameworks for Optical Sensing of VOCs via Refractive Index Modulation

Volatile Organic Compounds (VOCs) pose a significant threat to indoor air quality, contributing to a range of short and long-term health issues. The development of materials capable of selectively and sensitively detecting VOCs is therefore essential. Among various sensing strategies, optical detection based on refractive index (RI) modulation has emerged as a promising approach for detecting VOCs. However, accurate prediction of the dielectric and optical properties of crystalline materials using ab initio methods is computationally demanding, limiting their feasibility for large-scale screening. In this work, we present a scalable and predictive computational framework for evaluating the dielectric and optical properties of metal–organic frameworks (MOFs), with a focus on zeolitic imidazolate frameworks (ZIFs). We systematically benchmark a range of ab initio methods, including Coupled Perturbed Kohn–Sham (CPKS), time-dependent DFT (TDDFT), and density functional perturbation theory (DFPT), to assess their accuracy and computational efficiency in predicting response properties. Our analysis highlights the relative strengths and limitations of each approach, providing practical guidance for selecting methods in future studies. To enable efficient screening, we integrate a fragment-based approach developed by (Treger et al. (2023) Phys. Chem. Chem. Phys. 25, 19013–19023) with fully periodic DFT calculations. We apply this multitiered approach to ten chemically and structurally diverse ZIFs, varying in metal centers, linkers, and pore confinement. Our findings demonstrate that the fragment-based approach serves as a reliable surrogate for periodic methods, enabling accurate refractive index prediction at a fraction of the computational cost. Furthermore, we identify that frameworks with highly polarizable linkers and compact pore geometries exhibit the most significant RI shifts upon VOC adsorption, highlighting structure–property relationships that can guide the design of MOF-based optical sensors.