Wang Jingting, Lu Mingying, Chen Yongxing, Hao Guolin, Liu Bin, Tang Pinghua, Yu Lian, Wen Lei, Ji Haining
School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China.
Nanomaterials (Basel). 2023 Aug 9;13(16):2283. doi: 10.3390/nano13162283.
Molybdenum disulfide (MoS) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and -value (_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS materials with a reduction in the time and cost of trial and error.
二硫化钼（MoS）是一种层状过渡金属硫化合物半导体，因其制备成本低、稳定性好以及具有优异的物理化学、生物和机械性能，在光电子学和集成电路领域展现出广阔的应用前景。高质量、大尺寸且性能优异的MoS可通过化学气相沉积（CVD）法制备。然而，其制备过程复杂，所得MoS的面积难以控制。机器学习（ML）作为一种强大的工具，已在材料科学中得到广泛应用。基于此，本文构建了一个ML高斯回归模型，以探究用CVD法制备MoS材料的生长机制。通过结合拟合优度（r2）、均方误差（MSE）、皮尔逊相关系数（p）和皮尔逊相关系数的p值（_val）这四个指标对回归模型的参数进行评估。经过综合比较，发现当迭代次数为15时模型性能最佳。此外，利用所建立的模型对生长参数进行了特征重要性分析。结果表明，载气流量（Fr）、钼硫比（R）和反应温度（T）对MoS材料的CVD生长有至关重要的影响。使用最优模型对模拟数据集中185,900个实验条件下二硫化钼合成的尺寸进行预测，以选择合成大尺寸二硫化钼的最佳范围。此外，通过文献和实验结果对模型预测结果进行了验证。发现预测结果与文献和实验结果之间的相对误差较小。这些发现为MoS材料的制备提供了一种有效的解决方案，减少了试错的时间和成本。
