通过AI/ML驱动的聚合和共聚合利用数据和控制
Rigoberto Advincula1, Ilia Ivanov1, Rama Vasudevan1
1Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. advincularc@ornl.gov.
Faraday discussions
|October 14, 2025
概括
人工智能和机器学习 (AI/ML) 可以优化聚合物合成. 这种方法使用连续流反应器和实时监控来精确控制共聚合过程.
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
- 人工智能的人工智能
背景情况:
- 聚合物开发的传统方法通常是缓慢的,并且依赖于试错.
- 改善现有聚合物的性能对于各种应用至关重要.
- 人工智能/ML提供了一种强大的方法来加速材料的发现和优化.
研究的目的:
- 展示AI/ML协议,以优化聚合和共聚合.
- 为了实现目标的聚合物特性,而无需进行广泛的重制.
- 为自主聚合物制造建立一个框架.
主要方法:
- 利用AI/ML优化合成和制造.
- 使用带有集成传感器的自动驾驶连续流化学反应器.
- 通过在线监控和反循环实现实时ML.
- 使用ML精制经典方程,如梅奥-易斯方程 (MLE).
主要成果:
- 通过AI/ML证明了通过聚合物的层次优化协议.
- 最初的结果显示ML对MLE的改进以及对时间序列数据的分析.
- 建立了一个自主流动反应堆系统作为数据生成平台.
结论:
- 通过AI/ML,可以精确控制共聚合过程.
- 自主,人工智能引导的协议可以导致未来制造新型聚合物.
- 这项工作为数据驱动的加速材料科学奠定了基础.
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