Data61, CSIRO, Door 34 Goods Shed, Village St, Docklands VIC 3008, Australia.
Nanotechnology. 2017 Sep 20;28(38):38LT03. doi: 10.1088/1361-6528/aa82e5. Epub 2017 Jul 28.
Computational screening is key to understanding structure-function relationships at the nanoscale but the high computational cost of accurate electronic structure calculations remains a bottleneck for the screening of large nanomaterial libraries. In this work we propose a data-driven strategy to predict accuracy differences between different levels of theory. Machine learning (ML) models are trained with structural features of graphene nanoflakes to predict the differences between electronic properties at two levels of approximation. The ML models yield an overall accuracy of 94% and 88%, for energy of the Fermi level and the band gap, respectively. This strategy represents a successful application of established ML methods to the selection of optimum level of theory, enabling more rapid and efficient screening of nanomaterials, and is extensible to other materials and computational methods.
计算筛选是理解纳米尺度下结构-功能关系的关键，但准确的电子结构计算的高计算成本仍然是筛选大型纳米材料库的瓶颈。在这项工作中，我们提出了一种数据驱动的策略来预测不同理论水平之间的精度差异。使用石墨烯纳米片的结构特征来训练机器学习 (ML) 模型，以预测两个近似水平的电子性质之间的差异。ML 模型分别对费米能级的能量和能带隙的差异的预测精度达到 94%和 88%。该策略成功地将成熟的 ML 方法应用于最佳理论水平的选择，使纳米材料的筛选更快、更高效，并且可以扩展到其他材料和计算方法。
