Physicochemical Insights into Laser-Induced Graphene via Data-Driven Materials-Genome Analysis of Natural and Synthetic Precursors

Abstract

The Materials Genome (MG) approach aims to accelerate the development of functional carbon materials by quantitatively linking precursor chemistry, atomic structure, and material properties. This study used ReaxFF molecular dynamics simulations to examine laser-induced graphene (LIG) formation from seven typical precursors: natural precursors (cellulose, chitosan, lignin, and coal) and synthetic polymer precursors (PC, PEEK, and PI). Structural analysis showed that precursor chemistry significantly affects graphitization, defect topology, and network order, with distortions primarily associated with five- and seven-membered carbon rings. Effective bond-scission activation energy, derived from Arrhenius analysis, aligned well with experimental trends. Self-diffusion coefficients, calculated from mean-square displacement (MSD), showed systematic dependence on temperature and size, while the vibrational density of states (VDOS) provided further insights into atomic dynamics and order. To establish structure–property relationships, a descriptor-based MG framework combining structural, kinetic, and vibrational metrics was developed. Feature ranking via correlation analysis identified aromaticity, as indicated by the sp2/sp3 ratio, as the primary descriptor for diffusivity, with ring topology, porosity, and vibrational features playing secondary roles. These results connect precursor-derived structural descriptors with transport properties, providing a data-driven materials-genome perspective on LIG formation. The framework offers a systematic approach for screening precursor materials and understanding structure–property relationships in sustainable LIG production for sensing, catalysis, and electrochemical uses.