Computational modeling of lignin: from molecular structure to functional properties

Abstract

Lignin is one of the abundant aromatic biopolymers in nature, constituting approximately 15–30% of the lignocellulosic biomass and representing a major renewable source of aromatic carbon. Its high carbon content, high thermal stability, biodegradability, antioxidant property, and ability to absorb ultraviolet radiation make lignin an attractive precursor for many potential applications, such as for renewable carbon materials, plastics, additives, surfactants, and the resin industry. However, its large-scale valorisation remains limited due to its complex, heterogeneous structure and the limited ability of experimental techniques to resolve its molecular architecture and reaction mechanisms. Recent advancements in computer-based simulations, especially molecular dynamics (MD) and density functional theory (DFT), offer powerful tools to overcome these limitations by enabling molecular and atomic-level insight into lignin structure, properties, and transformation processes. These methods provide detailed insights into lignin reactivity, thermophysical behaviour, self-assembly, and catalytic depolymerisation that are often difficult to obtain experimentally. This review summarises recent advances in computational studies of lignin and its derivatives, highlighting how different modelling approaches have been used to investigate the various physicochemical properties and reaction mechanisms. In addition, the limitations and challenges associated with current modelling strategies are discussed, and future research directions, including multiscale modelling and machine-learning-based approaches, are outlined. Overall, this review aims to provide a comprehensive overview of how computational methods advance lignin research and support the development of sustainable lignin-based materials.