Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using differential evolution algorithm
The development of flexible composites is of great significance in flexible electronics field. In combination with machine learning technology, the introduction of artificial intelligence in flexible material 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 Differential Evolution (DE) algorithm was applied to estimate the electrical properties of flexible Ag/poly (amic acid) (PAA) composite structure and to develop the flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO3, the concentration and the reduction time of NaBH4 are set to the input parameters, the product of the sheet resistance of Ag/PAA film and the processing time are set to the output information. To overcome the situation that the BP solution process could fall into the local optimum, the initial threshold and the weight of BP and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, the machine learning model with very high accuracy was established and the relative errors of predictions less than 1.96% were achieved. In term of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted, respectively. To identify the availability and applicability of the proposed algorithm, the strain gauge sensor, the triboelectric nanogenerator (TENG) and the capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work exhibits that the machine learning can be used to quickly optimize the process and provide the guidance for material and process design, which is of significance for the development of flexible materials and devices.