Audus Debra J, de Pablo Juan J
Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland, 20899 USA.
The Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois, 60637 USA.
ACS Macro Lett. 2017 Oct;6(10):1078-1082. doi: 10.1021/acsmacrolett.7b00228. Epub 2017 Sep 15.
We are entering an era where large volumes of scientific data, coupled with algorithmic and computational advances, can reduce both the time and cost of developing new materials. This emerging field known as materials informatics has gained acceptance for a number of classes of materials, including metals and oxides. In the particular case of polymer science, however, there are important challenges that must be addressed before one can start to deploy advanced machine learning approaches for designing new materials. These challenges are primarily related to the manner in which polymeric systems and their properties are reported. In this viewpoint, we discuss the opportunities and challenges for making materials informatics as applied to polymers, or equivalently polymer informatics, a reality.
我们正在进入一个新时代,大量科学数据与算法和计算技术的进步相结合,可以减少开发新材料的时间和成本。这个新兴领域被称为材料信息学,已经在包括金属和氧化物在内的多种材料类别中得到认可。然而,在聚合物科学领域,在开始部署先进的机器学习方法来设计新材料之前,必须解决一些重要挑战。这些挑战主要与聚合物体系及其性能的报告方式有关。在本文观点中,我们讨论了将材料信息学应用于聚合物(即聚合物信息学)成为现实所面临的机遇和挑战。