一种模型整体方法使得在低数据模式下,能够对化学分解式热进行数据驱动的属性预测
Yasmeen S AlFaraj1, Somesh Mohapatra2, Peyton Shieh1
1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States of America.
ACS central science
|October 2, 2023
概括
这项研究引入了一种机器学习方法来预测热性质,从而使可调节性特征的可持续塑料的设计成为可能. 该方法准确地预测了具有新型组件的聚二二烯热的玻璃过渡温度.
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
- 计算化学计算化学
背景情况:
- 热电由于其复杂的组成和缺乏预测性质知识,造成了可持续性挑战.
- 开发具有可调节性质的可拆解热对环境可持续性至关重要.
- 计算方法与无形的,多元件的热稳固系统作斗争.
研究的目的:
- 使用机器学习开发一种用于热性质的预测模型.
- 加速发现具有可控解构能力的可持续热材料.
- 根据分子构建块和配方来实现属性预测.
主要方法:
- 使用了结合实验和机器学习 (ML) 的闭环策略.
- 使用了101个聚二cyclopentadiene (pDCPD) 热固体示例的数据集.
- 分子特征和配方变量被用作ML模型输入,不确定性量化通过组合.
主要成果:
- 准确预测玻璃过渡温度 (Tg) 的pDCPD热聚与可切割的双功能西乙烯 (BSE) 共同体/交叉连接器.
- 预测在不同的BSE组合下达到了<15°C的准确度.
- ML模型有效地处理了多组件,无形热稳固系统.
结论:
- 这种数据驱动的方法有助于从分子组件中预测热稳固性质.
- 该战略加速了先进塑料,和复合材料的发现.
- 能够设计具有更好的功能和受控的可拆解性的热,以实现可持续性.
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