Yuan Fenglin, Mueller Tim
Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
Sci Rep. 2017 Dec 14;7(1):17594. doi: 10.1038/s41598-017-17535-3.
The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large material data sets to identify models that can rapidly predict a given property. In this paper, we demonstrate the use of genetic programming to generate simple models of dielectric breakdown based on 82 representative dielectric materials. We identified the band gap E and phonon cut-off frequency ω as the two most relevant features, and new classes of models featuring functions of E and ω were uncovered. The genetic programming approach was found to outperform other approaches for generating models, and we discuss some of the advantages of this approach.
能够快速预测材料特性的模型的识别，能够对大量材料进行快速筛选，并有助于新材料的设计。计算研究人员面临的主要挑战之一是确定分析大型材料数据集的最佳方法，以识别能够快速预测给定特性的模型。在本文中，我们展示了使用遗传编程基于82种代表性介电材料生成介电击穿的简单模型。我们确定带隙E和声子截止频率ω为两个最相关的特征，并发现了以E和ω的函数为特征的新模型类别。发现遗传编程方法在生成模型方面优于其他方法，并且我们讨论了这种方法的一些优点。
