Replication Data for "Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques"

The mechanical properties of multiscale poly(3-hydroxybutyrate) (P3HB)-based nanocomposites reinforced with different concentrations of multiwalled carbon nanotubes (MWCNTs), WS2 nanosheets and sepiolite (SEP) nanoclay have been predicted. The nanocomposites were prepared via solution casting. SEM images revealed that the three nanofillers were homogenously and randomly dispersed into the matrix. A synergistic reinforcement effect was attained, resulting in an unprecedented stiffness improvement of 132% upon addition of 1:2:2 wt% SEP:MWCNTs:WS2. Conversely, the increments in strength were only moderates (up to 13.4%). A beneficial effect in the matrix ductility was also found due to the presence of both nanofillers. Four ML approaches, Recurrent Neural Network (RNN), RNN with Levenberg’s algorithm (RNN-LV), decision tree (DT) and Random Forest (RF), were applied. The correlation coefficient (R2), mean absolute error (MAE) and mean square error (MSE) were used as statistical indicators to compare their performance. The best performing model for the Young’s modulus was RNN-LV with 3 hidden layers and 50 neurons in each layer, while for the tensile strength was the RF model using a combination of 100 estimators and a maximum depth of 100. An RNN model with 3 hidden layers was the most suitable to predict the elongation at break and impact strength, with 90 and 50 neurons in each layer, respectively. The highest correlation (R2 of 1 and 0.9203 for the training and test set, respectively) and the smallest errors (MSE of 0.13 and MAE of 0.31) were obtained for the prediction of the elongation at break. The developed models represent a powerful tool for the optimization of the mechanical properties in multiscale hybrid polymer nanocomposites, saving time and resources in the experimental characterization process.

本研究已针对不同浓度多壁碳纳米管（multiwalled carbon nanotubes，MWCNTs）、二硫化钨（WS₂）纳米片与海泡石（sepiolite，SEP）纳米黏土增强的多尺度聚(3-羟基丁酸酯)（poly(3-hydroxybutyrate)，P3HB）基纳米复合材料的力学性能开展了预测。该类纳米复合材料通过溶液浇铸法制备，扫描电子显微镜（SEM）成像结果显示，三种纳米填料均均匀且随机地分散于基体当中。研究发现体系存在协同增强效应：当添加质量比为1:2:2的SEP:MWCNTs:WS₂时，材料刚度实现了132%的空前提升；与之相对，材料强度仅实现小幅提升（最高达13.4%）。此外，多种纳米填料的引入还可改善基体的延性。本研究采用了四种机器学习方法：循环神经网络（Recurrent Neural Network，RNN）、结合莱文贝格算法的循环神经网络（RNN-LV）、决策树（decision tree，DT）与随机森林（Random Forest，RF），并以相关系数（correlation coefficient，R²）、平均绝对误差（mean absolute error，MAE）与均方误差（mean square error，MSE）作为性能评价指标，对比各模型的表现。结果表明，用于预测杨氏模量（Young’s modulus）的最优模型为含3个隐藏层、每层50个神经元的RNN-LV模型；用于预测拉伸强度（tensile strength）的最优模型为采用100棵决策树、最大深度为100的随机森林模型；而用于预测断裂伸长率（elongation at break）与冲击强度（impact strength）的最优模型则为含3个隐藏层的RNN模型，其每层神经元数分别为90与50。在断裂伸长率的预测任务中，模型取得了最高的相关系数（训练集与测试集的R²分别为1与0.9203）以及最小的误差（均方误差MSE为0.13，平均绝对误差MAE为0.31）。本研究所构建的模型可为多尺度混杂聚合物纳米复合材料的力学性能优化提供有力工具，有效节省实验表征过程中的时间与资源。