Predicting Optical Properties of Cellulose-Based Materials Using Multiscale Modeling

High mechanical strength along with unique optical properties in biomaterials are often attributed to the presence of chiral multilayer ordered structures with the complex hierarchical organization. One such example is cellulose nanocrystals (CNC)-based materials, which are characterized by selective reflection from the helical structures formed by CNCs when the chiral pitch is in the visible range, resulting in iridescent colored films. Several factors have been reported to affect optical properties of CNC materials, such as the CNC dimensions, density/packing fraction, chemical modifications of CNCs, helical pitch and handedness of the individual CNCs, the presence of co-solutes or other additives, humidity, and mechanical stretching. However, due to the multitude of factors that may influence the optical properties of CNCs, the delineation of structure-optical property relationships of CNCs has been a challenge. We use systematic multiscale molecular modeling and machine learning to gain insights into self-assembly and other structural mechanisms contributing to the stress-optical relations (SOR) of CNC-based materials. We used a combination of electronic structure calculations and all-atom and coarse-grained molecular dynamics simulations to predict the self-assembly of CNC as a function of surface chemistry and environment, structural order parameters, and optical properties, such as refractive indices, in-plane birefringence, and out-of-plane birefringence as a function of stress. We then examined the correlation and sensitivity of these properties to factors of interest, such as stretch ratio, morphology, surface functionalization, and wavelength. We used order parameters from simulation and experiment to train the machine learning model and to further advance the optimization of optomechanical properties. The results from our study will help discover the design rules of chiral natural materials with enhanced mechanical, optical, transport, and photonic functionalities relevant to light and matter manipulation and conversion.