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Machine learning and genetic algorithm prediction of energy differences between electronic calculations of graphene nanoflakes

机译：石墨烯纳米薄膜电子计算能量差异的机器学习与遗传算法预测

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摘要

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.
机译：计算筛选是了解纳米级结构功能关系的关键，但精确的电子结构计算的高计算成本仍然是筛选大纳米材料文库的瓶颈。 在这项工作中，我们提出了一种数据驱动的策略，以预测不同级别的理论之间的准确性差异。 机器学习（ML）模型培训具有石墨烯纳米薄片的结构特征，以预测两个近似水平的电子性质之间的差异。 ML模型分别产生94％和88％的总精度，分别用于费米水平和带隙的能量。 该策略代表了建立的ML方法的成功应用于选择最佳理论水平，使得能够更快和高效地筛选纳米材料，并且可用于其他材料和计算方法。

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