绿色溶剂的机器学习:评估,选择和替代
Rohan Datta1, Janhavi Nistane2, Abhishek Sose2
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 17, 2025
概括
一个新的数据驱动管道评估溶剂的可持续性,创建了最大的绿色溶剂数据库. 这加快了对环境友好的溶剂的发现,这些溶剂的性能与危险的溶剂相匹配.
科学领域:
- 绿色化学 绿色化学
- 计算化学计算化学
- 环境科学 环境科学
背景情况:
- 由于严格的环境法规,对可持续溶剂的需求日益增加.
- 现有的解决方案选择指南 (SSG) 的范围和数据可用性有限.
- 确定更绿色的替代品需要平衡复杂的标准,如可持续性,成本和性能.
研究的目的:
- 开发一个数据驱动的管道来评估溶剂的可持续性和确定更绿色的替代品.
- 扩大评估溶剂库,超出当前指南的有限范围.
- 创建一个全面的绿色溶剂指标数据库.
主要方法:
- 在GlaxoSmithKline溶剂可持续性指南 (GSK SSG) 上训练和评估高斯过程回归 (GPR) 和GPT模型.
- 利用 GPR 预测 10,189 种溶剂的绿色度指标,创建了 GreenSolventDB.
- 将预测指标与汉森溶解度参数结合起来,以找到合适的绿色替代品.
主要成果:
- 在预测溶剂绿色度方面,GPR模型略高于基于语言的GPT模型.
- 绿色溶剂数据库 (GreenSolventDB) 是绿色溶剂指标的最大的公共数据库.
- 预测的替代品危险的溶剂,如和二甲基乙醇显示出与已知的替代品很好地对齐.
结论:
- 开发的框架有效量化了溶剂的可持续性,并确定了可行的更绿色替代品.
- 这种方法显著加速了环保溶剂的发现和采用.
- 绿色SolventDB为研究人员和工业提供了宝贵的资源.
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