Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning,ACS Sustainable Chemistry & Engineering

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Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning
ACS Sustainable Chemistry & Engineering ( IF 7.1 ) Pub Date : 2021-02-15 , DOI: 10.1021/acssuschemeng.0c08856
Qiang Li ₁ , Gerhard Wittreich ₂ , Yifan Wang ₂ , Himaghna Bhattacharjee ₂ , Udit Gupta ₁ , Dionisios G. Vlachos _{1,

2}

Affiliation

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Finally, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

中文翻译：

通过密度泛函理论，基团加和法和机器学习法对复杂木质素结构进行精确的热化学

对木质素结构和键解离能的分子水平的了解可以促进解聚技术。尽管如此，由于缺乏数据库和代理模型的简化，目前该信息仍然受到限制。在这里，对木质素聚合物中七个常见键的取代作用进行了系统的研究。使用自动反应网络生成器来创建结构数据库。引入了一种基于主成分分析（PCA）描述符的新的组可加性（GA）模型，并在M06-2X / 6-311 ++ G（d，p）级别对4100种物种的气相密度泛函理论数据进行了训练。。氢键，局部空间和非芳香环的贡献也被纳入。最后，

更新日期：2021-03-01

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