4.6 Article

Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

期刊

SOFT MATTER
卷 17, 期 33, 页码 7697-7707

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/d1sm01012c

关键词

-

资金

  1. Institute for Computational and Data Sciences
  2. Materials Research Institute at the Pennsylvania State University

向作者/读者索取更多资源

This study applies unsupervised machine learning to analyze soft matter systems, providing new insights into the structure of large-scale, disordered aggregates formed by sequence-defined macromolecules. By classifying the global aggregate structure directly using descriptions of local environments, a deeper understanding of the possible self-assembled structures and their relationships is obtained.
We apply a recently developed unsupervised machine learning scheme for local environments [Reinhart, Comput. Mater. Sci., 2021, 196, 110511] to characterize large-scale, disordered aggregates formed by sequence-defined macromolecules. This method provides new insight into the structure of these disordered, dilute aggregates, which has proven difficult to understand using collective variables manually derived from expert knowledge [Statt et al., J. Chem. Phys., 2020, 152, 075101]. In contrast to such conventional order parameters, we are able to classify the global aggregate structure directly using descriptions of the local environments. The resulting characterization provides a deeper understanding of the range of possible self-assembled structures and their relationships to each other. We also provide detailed analysis of the effects of finite system size, stochasticity, and kinetics of these aggregates based on the learned collective variables. Interestingly, we find that the spatiotemporal evolution of systems in the learned latent space is smooth and continuous, despite being derived from only a single snapshot from each of about 1000 monomer sequences. These results demonstrate the insight which can be gained by applying unsupervised machine learning to soft matter systems, especially when suitable order parameters are not known.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据