期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 186, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.commatsci.2020.110067
关键词
Genetic algorithm; Machine learning; Glass transition temperature; Bandgap; Polymer
资金
- National Science Foundation [1743418]
- Multidisciplinary University Research Initiative grant from the Office of Naval Research [N00014-17-1-2656]
The combination of genetic algorithm and ML-based predictive models has been utilized to design polymers with extreme performance criteria, creating 132 chemically unique polymers with high T-g and E-g. The algorithm also reveals chemical guidelines for composing polymers with extreme thermal and electrical performance metrics.
Data driven or machine learning (ML) based methods have been recently used in materials science to provide quick material property predictions. Although powerful and robust, these predictive models are still limited in terms of their applicability towards the design of materials with target property or performance objectives. Here, we employ a nature-mimicking optimization method, the genetic algorithm, in tandem with ML-based predictive models to design polymers that meet practically useful, but extreme, property criteria (i.e., glass transition temperature, T-g > 500 K and bandgap, E-g > 6 eV). Analogous to nature, the characteristic properties of a polymer are assumed to be determined by the constituting types and sequence of chemical building blocks (or fragments) in the monomer unit. Evolution of polymers by natural operations of crossover, mutation, and selection over 100 generations leads to creation of 132 new (as compared to 4 already known cases) and chemically unique polymers with high T-g and E-g. Chemical guidelines on what fragments make up polymers with extreme thermal and electrical performance metrics have been selected and revealed by the algorithm. The approach presented here is general and can be extended to design polymers with different property objectives.
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。
推荐
暂无数据