聚合物的反向设计,包括静态测量和链架构
1Department of Intelligent Systems, Delft University of Technology Delft 2629 HZ The Netherlands j.m.weber@tudelft.nl.
Chemical science
|December 19, 2024
概括
本研究介绍了一种机器学习模型,用于生成新型合成聚合物,包括它们的复杂结构和单体组合. 这加速了用于光催化等应用的先进材料的发现.
科学领域:
- 聚合物科学与工程 聚合物科学与工程
- 材料 信息学 信息学
- 计算化学计算化学
背景情况:
- 对具有增强性能的新型合成聚合物有很高的需求.
- 由于结构复杂性和庞大的设计空间,聚合物发现的挑战.
- 在聚合物生成设计中,当前机器学习方法的局限性,特别是有限的标记数据.
研究的目的:
- 推进机器学习引导的分子设计,以加速聚合物发现.
- 开发一种能够设计不仅是重复单元,而且还可以设计具有定义的固态度和链架构的单体组合的生成模型.
- 为了能够 de novo生成具有可控性质的共聚合物结构.
主要方法:
- 开发一种新的变异自编码器 (VAE) 架构.
- VAE编码聚合物图形并解码字符串表示.
- 实施半监督学习方法来处理部分标记的数据集.
主要成果:
- 该模型成功地学习了一个连续的和有组织的潜空间 (LS).
- 证明了共聚合物结构的 de novo 生成,包括不同的单体固态度和链架构.
- 在反向设计案例研究中成功应用,用于发现用于生产的结合性共聚物光催化剂.
结论:
- 拟议的VAE模型有效地解决了聚合物生成设计的挑战,特别是有限的数据.
- 该模型有助于发现具有定制性质的新型聚合物结构.
- 这种方法加速了先进材料的识别,例如用于生产的光催化剂.
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