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通过自动化共聚合物合成实现的19种MRI剂的机器学习引导的发现
Marcus Reis1, Filipp Gusev2,3, Nicholas G Taylor1
1Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.
Journal of the American Chemical Society
|October 12, 2021
概括
计算机引导的发现加速了19F磁共振成像 (MRI) 剂的聚合物开发. 机器学习发现了超越当前标准的新型共聚合物组成,克服了合成挑战.
科学领域:
- 聚合物科学
- 材料科学
- 计算化学
背景情况:
- 现代聚合物科学面临着巨大的化学空间所带来的挑战,阻碍了系统的结构-属性关系研究.
- 开发像19F磁共振成像 (MRI) 等先进材料是复杂的,
研究的目的:
- 克服复杂材料的传统聚合物合成和表征的局限性.
- 采用计算机引导的方法,结合自动合成和机器学习来发现材料.
主要方法:
- 开发了一个软件控制的连续聚合物合成平台,用于自动化的代实验计算循环.
- 使用机器学习 (ML) 来探索六个变量组成的空间,并确定非直观的设计标准.
- 在有限的整体组合空间 (<0.9%) 中合成了397种独特的共聚物组合物.
主要成果:
- 通过ML驱动的探索确定了非直观的设计标准.
- 发现了10多种新型共聚合物成分,
- 在复杂的聚合物化学空间中证明了计算机引导方法的效率.
结论:
- 计算机引导的材料发现,集成自动合成和机器学习,是推动聚合物科学发展的强大策略.
- 这种方法有效地解决了共聚物开发中的多维性挑战.
- 已识别的共聚物对19FMRI剂的应用具有显著的前景.
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