Computational Evaluation, characterization and screening of materials and molecules for sustainable applications

Computational simulation is wildly used for materials evaluation, characterization and property prediction, playing a critical role in advancing material science. In this thesis, we focus on three sustainable applications: suppression of lignin repolymerization, CO2 adsorption, and photocatalytic CO2 conversion, approached from a computational aspect. Density Functional Theory (DFT) was used to evaluate and characterize an initial set of candidate molecules and materials. We developed and applied a systematic computational framework that integrates chemical model construction, target property evaluation, reaction pathway analysis, performance differentiation, and high-throughput screening. This multiscale approach enabled the identification of promising cation scavengers for biomass pretreatment, functional molecules and MOFs with enhanced CO₂ binding affinities, and photocatalysts with suitable band structures and reaction energetics for solar-driven CO₂ conversion.
Our results not only provide insight into the underlying structure-property relationships but also offer valuable guidance for experimental validation and future material design aimed at sustainable development and carbon mitigation.