State Key Laboratory of Advanced Processing and Recycling of Non-ferrous Metal, Department of Materials Science and Engineering, Lanzhou University of Technology, Lanzhou 730050, PR China.
ACS Appl Mater Interfaces. 2023 Mar 8;15(9):12462-12472. doi: 10.1021/acsami.2c15957. Epub 2023 Feb 24.
A first-principles approach is a powerful means of gaining insight into the intrinsic structure and properties of materials. However, with the implementation of material genetic engineering, it is still a challenging road to discover materials with high satisfaction. One alternative is to employ machine-learning techniques to mine data and predict performance. In this present contribution, the method is taken to predict the band gap opening value of graphene in a heterostructure. First, the data of 2076 binary compounds in the Materials Project library are used to achieve visual dimensionality reduction of the data set through a t-distributed stochastic neighbor embedding (t-SNE) algorithm in unsupervised learning. Then, a series of semiconductor components are screened out and form heterostructures with graphene. Second, by means of the ensemble learning EXtreme Gradient Boost (XGBoost) algorithm and support vector machine (SVM) technology, two prediction frameworks are built to predict the band gap opening value of the graphene in the system. Finally, density functional theory (DFT) is used to calculate the energy band and density of states for comparison. Analysis shows that the prediction model has an accuracy rate of 88.3%, and there is little difference between prediction results and calculation results. We anticipate that this framework model would have fascinating applications in predicting the electronic properties of various multiphase materials.
基于第一性原理的方法是深入了解材料内在结构和性质的有力手段。然而，随着材料基因工程的实施，发现令人满意的高满足度材料仍然是一条具有挑战性的道路。一种替代方法是利用机器学习技术挖掘数据并预测性能。在本研究中，采用该方法预测了石墨烯在异质结构中的带隙开值。首先，使用 Materials Project 库中的 2076 种二元化合物的数据，通过无监督学习中的 t 分布随机邻嵌入（t-SNE）算法实现数据集的可视化降维。然后，筛选出一系列半导体组件并与石墨烯形成异质结构。其次，通过集成学习极端梯度提升（XGBoost）算法和支持向量机（SVM）技术，构建了两个预测框架来预测体系中石墨烯的带隙开值。最后，使用密度泛函理论（DFT）计算能带和态密度进行比较。分析表明，预测模型的准确率为 88.3%，预测结果与计算结果之间的差异很小。我们预计该框架模型将在预测各种多相材料的电子性质方面具有迷人的应用前景。
