机器学习用于聚合物设计,以提高基于蒸发的有机回收
Meiqi Yang1,2, Jun-Jie Zhu1,2, Allyson L McGaughey1,2,3
1Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States.
机器学习模型加速了用于透气 (PV) 膜分离的高性能聚合物的发现. 这种方法克服了透性-选择性权衡,降低了成本,提高了有机脱水和回收的效率.
科学领域:
- 膜科学与工程 膜科学与工程
- 聚合物科学 聚合物科学
- 计算化学的计算化学
背景情况:
- 透气 (PV) 对于有机脱水,恢复和升级至关重要.
- 目前的膜材料面临着一个关键的透性-选择性权衡,限制了工艺效率.
- 开发新型高性能聚合物对于推进光伏技术至关重要.
研究的目的:
- 引入机器学习 (ML) 模型,用于识别光伏膜的有前途的聚合物候选物.
- 与传统方法相比,提高聚合物发现的效率和降低成本.
- 为了加速为特定的分离任务量身定制的膜的设计.
主要方法:
- 利用了最大的可用的光伏数据集,包括聚合物指纹,膜结构,操作条件和溶液特性.
- 采用缩小维度,缺失数据归算,可控随机性和数据泄漏预防,以实现可靠的模型开发.
- 优化了LightGBM模型来预测分离因子和总流量.
主要成果:
- 优化的LightGBM模型实现了0.447的低根平均平方误差 (RMSE) 对于分离因子和0.360的总流量在对数尺度上.
- 使用ML模型选了大约100万种假设的聚合物.
- 已确定具有高预测透分离指数 (>30) 和良好的合成可访问性 (<3.7) 的聚合物用于酸提取.
结论:
- 机器学习显著加快了用于蒸发膜的高潜力聚合物的识别.
- 开发的ML模型有效地解决了透性-选择性挑战.
- 这项研究展示了ML在为高效的分离过程设计定制膜材料方面的潜力.
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