通过机器学习预测利格宁的溶解度
Changhang Zhang1, Chenxin Sun2, Xinyu Wu1
1Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, China.
Biomacromolecules
|October 16, 2025
概括
这项研究使用机器学习来预测红素溶解度,克服了其应用中的挑战. 准确的预测有助于选择绿色溶剂和制备均的素材料.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 聚合物科学 聚合物科学
- 计算化学的计算化学
背景情况:
- 红是一种可再生资源,具有潜在的应用.
- 挑战包括红素的多分散性和可变溶解性.
- 预测红素溶解度对于其实际使用至关重要.
研究的目的:
- 开发一种机器学习模型,用于预测素溶解度.
- 了解素分子结构如何影响溶解度.
- 为了指导绿色溶剂的选择,用于素加工.
主要方法:
- 使用GPC和HSQCNMR对100种素的表征.
- 素分子结构的构建.
- 机器学习模型集成结构和量子化学数据.
- 使用COSMOtherm软件进行溶解度预测.
- 对特征的重要性进行SHAP分析.
主要成果:
- 在预测红素溶解度 (R2值高达0.987) 中获得了高准确度.
- 确定了影响素溶解性的关键分子特征.
- 证明了该模型在不同溶剂中的有效性.
- 提供了对结构-可溶性关系的见解.
结论:
- 机器学习可以根据分子结构准确地预测红素溶解度.
- 了解结构-可溶性关系有助于选择溶剂.
- 这种方法有助于开发单分散性木质素和可持续的溶剂系统.
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