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  2. 生物能源和生物产品循环经济的分子模拟和人工智能
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  2. 生物能源和生物产品循环经济的分子模拟和人工智能

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生物能源和生物产品循环经济的分子模拟和人工智能

Jeremy C Smith1, Micholas Dean Smith1, Shih-Hsien Liu1

  • 1University of Tennessee / Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee.

Biophysical journal
|September 7, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

分子动力学 (MD) 和机器学习 (ML) 通过克服纤维素重复性加速循环生物经济. 这些计算方法提高了生物质解构和生物材料设计的可持续性燃料和产品.

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科学领域:

  • 生物技术和可持续材料科学
  • 计算化学和材料信息学

背景情况:

  • 循环生物经济的目标是实现碳中和和零废物,而纤维素生物质是关键资源.
  • 有效地将固的纤维素酸转化为有价值的生物产品仍然是一个重大挑战.
  • 目前用于生物质解构和生物材料生产的方法可能耗费大量能源且效率低下.

研究的目的:

  • 概述分子动力学 (MD) 模拟和机器学习 (ML) 在推进纤维素循环生物经济中的关键作用.
  • 突出MD和ML/AI如何解决纤维素重复性并改善生物质衍生材料的特性.
  • 在加速生物经济发展方面展示量子力学 (QM) 方法,MD和ML之间的协同作用.

主要方法:

  • 使用分子动力学 (MD) 模拟来理解生物质解构机制.
  • 应用机器学习 (ML) 和人工智能 (AI) 来设计高效的生物质预处理策略.
  • 使用量子力学 (QM) 方法和MD模拟来生成用于培训生物质和生物材料的ML模型的数据.
  • 利用机器学习模型指导材料和工艺优化的实验努力.

主要成果:

  • MD和ML/AI有效地解决了纤维素复原性,使溶解纤维素,纤维素和半纤维素的有效生产成为可能.
  • 计算方法改善了基于生物质的材料的设计和制造,提高了它们的性能.
  • 质量管理和MD模拟为生物材料设计和解构提供了关键的机械洞察和数据.
  • 机器学习模型加快了生物经济应用的最佳材料,溶剂和过程参数的识别.
  • 结论:

    • MD和ML是克服纤维素循环生物经济的基本挑战的必不可少的工具.
    • 这些计算方法显著加速了可持续生物燃料和生物材料的开发.
    • 整合QM,MD和ML为实现零废弃,碳中和生物经济提供了强大的途径.