Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub.

中文翻译：

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LigninGraphs：使用多尺度图建模确定木质素结构

木质素是一种芳香族生物聚合物，存在于无处不在的木质生物质来源中。设计和优化木质素增值过程需要对木质素结构有基本的了解。实验表征技术，例如二维异核单量子相干 (HSQC) 核磁共振 (NMR) 光谱，可以阐明聚合物分子的全局特性。计算机模型可以通过在分子和原子尺度上表示结构来扩展实验的分辨率。我们介绍了一种基于图的多尺度建模框架，用于木质素结构生成和可视化。该框架采用加速无拒绝聚合和分层 Metropolis Monte Carlo 优化算法。我们根据文献和杨木、松木和草本木质素的新实验 NMR 数据获得各种木质素原料的结构库。该框架可以指导研究人员实现可行的木质素结构、有效的太空探索和未来的动力学建模。它在 Python 中的软件实现，LigninGraphs，是开源的，可在 GitHub 上获得。