机器学习模型用于预测不同度和温度的聚合物在溶剂中的溶解度
Mona Amrihesari1, Joseph Kern2, Hilary Present3
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
The journal of physical chemistry. B
|December 12, 2024
概括
这项研究引入了一个新的数据集和模型来预测聚合物溶解度,提高材料设计. 这种先进的模型提供了比以前的方法更详细的可溶性分类.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物化学 聚合物化学
- 计算化学的计算化学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 对于加速新材料设计至关重要.
- 聚合物溶解性是开发新配方和加工技术的关键性质.
- 现有的聚合物溶解性的预测模型受到实验数据不足的限制.
研究的目的:
- 使用Crystal16的度测量,开发一个增强的聚合物溶液行为数据集.
- 训练一种能够在各种条件下预测聚合物溶液行为的预测模型.
- 为了更好的预测颗粒度,将聚合物/溶剂对分为三个不同的可溶性类别.
主要方法:
- 使用Crystal16的度测量,收集了各种聚合物溶液的高质量百分比传输数据.
- 开发并训练了一种AI/ML模型,使用生成的数据集来预测在多种温度和度下传输数据.
- 基于预测的溶解度分类的聚合物/溶剂对,超越了二元溶剂/不溶剂分类.
主要成果:
- 创建了一个全面的聚合物溶液行为数据集,包括各种聚合物,溶剂,度和温度.
- 成功训练了一个准确预测实验传输数据的模型.
- 实现了三类可溶性分类,与以前的二进制模型相比,提供了更详细的细节.
结论:
- 开发的数据集和预测模型显著提高了预测聚合物可溶性的能力.
- 该模型能够处理多重度,温度和部分可溶性,这对于工业应用非常有价值.
- 这项工作为配方人员和工艺设计人员提供了一种更加细致和实用的方法来预测可溶性.
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