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Layer-by-layer Synthesis and Transfer of Freestanding Conjugated Microporous Polymer Nanomembranes

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Published on: December 15, 2015

机器学习用于逐层纳米过膜性能预测和聚合物候选物探索.

Chen Wang¹, Li Wang², Hanwei Yu¹

¹School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, 2007, Australia.

|December 27, 2023

概括

机器学习模型准确地预测了层次 (LBL) 纳米过 (NF) 膜性能. 这种方法确定了23个有前途的聚合物候选物,用于制造高性能NF膜.

关键词:

一层一层的膜层.机器学习机器学习纳米过的纳米过方法透性透性的聚合物的聚合物.选择性的选择性

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科学领域:

材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.
计算化学的计算化学

背景情况:

纳米过 (NF) 膜性能对于水处理和分离过程至关重要.
预测NF膜性能和识别最佳聚合物候选者传统上涉及广泛的实验.
开发高效准确的NF膜设计方法对于推进分离技术至关重要.

研究的目的:

建立基于机器学习 (ML) 的模型,用于预测层次 (LBL) 纳米过 (NF) 膜性能.
探索和确定用于高性能LBL NF膜制造的新型聚合物候选物.
为加速发现先进的NF材料提供计算框架.

主要方法:

开发了四种ML模型 (线性,随机森林,增强树,极度梯度增强) 来预测膜透性和选择性.
使用沙普利增材扩张 (SHAP) 方法分析制造条件和聚合物结构对膜性能的影响.
聚合物使用摩根指纹来表示,数据库搜索根据SHAP衍生的参考指纹确定了潜在的候选者.

主要成果:

极端梯度增强 (XGBoost) 模型实现了膜透性 (R2:0.99) 和选择性 (R2:0.80) 的高预测精度.
SHAP分析揭示了影响膜透性和选择性的关键原子组,使得参考聚合物指纹的构建成为可能.
选了204种潜在的聚合物,从而选择了23种有前途的聚合物候选物用于LBL NF膜制造.

结论:

机器学习模型,特别是XGBoost,为准确的LBL NF膜性能预测提供了一个强大的工具.
基于SHAP的方法有效地将聚合物结构与膜性能联系起来,指导新材料的合理设计.
本研究提出了一种新的计算策略,以加快用于NF应用的高性能聚合物的探索和发现,并提供公开可用的代码.