Zhang Mengyao, Li Jia, Kang Ling, Zhang Nan, Huang Chun, He Yaqin, Hu Menghan, Zhou Xiaofeng, Zhang Jian
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
Nanoscale. 2020 Feb 14;12(6):3988-3996. doi: 10.1039/c9nr09146g. Epub 2020 Feb 4.
The development of flexible composites is of great significance in the flexible electronic field. In combination with machine learning technology, the introduction of artificial intelligence to flexible materials design, synthesis, characterization and application research will greatly promote the flexible materials research efficiency. In this study, the back propagation (BP) neural network based on the differential evolution (DE) algorithm was applied to determine the electrical properties of the flexible Ag/poly (amic acid) (PAA) composite structure and to develop flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO, and the concentration and reduction time of NaBH are set as input parameters, and the product of the sheet resistance of the Ag/PAA film and the processing time are set as output information. To overcome the situation whereby the BP neural network solution process could fall into the local optimum, the initial threshold and the weight of the BP neural network and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, a machine learning model with very high accuracy was established and relative errors of predictions less than 1.96% were achieved. In terms of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted. To identify the availability and applicability of the proposed algorithm, a strain gauge sensor, a triboelectric nanogenerator (TENG) and a capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work shows that machine learning can be used to quickly optimize the process and provide guidance for material and process design, which is of significance for the development of flexible materials and devices.
柔性复合材料的发展在柔性电子领域具有重要意义。结合机器学习技术，将人工智能引入柔性材料的设计、合成、表征及应用研究中，将极大地提高柔性材料的研究效率。在本研究中，基于差分进化（DE）算法的反向传播（BP）神经网络被用于确定柔性Ag/聚酰胺酸（PAA）复合结构的电学性能，并开发适用于不同应用的柔性材料。在机器学习模型中，将PAA的浓度、AgNO₃的离子交换时间以及NaBH₄的浓度和还原时间设置为输入参数，将Ag/PAA薄膜的方阻与处理时间的乘积设置为输出信息。为克服BP神经网络求解过程可能陷入局部最优的情况，采用DE算法对BP神经网络的初始阈值、权重以及数据导入模型进行优化。利用1077个学习样本和49个预测样本，建立了具有很高精度的机器学习模型，预测的相对误差小于1.96%。基于该模型，预测了适用于应变传感器和电极的Ag/PAA复合材料的优化制备条件。为验证所提算法的有效性和适用性，利用优化后的工艺参数成功制备了应变片式传感器、摩擦纳米发电机（TENG）和电容式压力传感器阵列。这项工作表明，机器学习可用于快速优化工艺，并为材料和工艺设计提供指导，这对柔性材料和器件的发展具有重要意义。
