Fig. 1. Schematic diagram of the neural network structure: (a) Convolutional neural network, INPUT is the data entry, OUTPUT is the learning result, and the padding way is SAME; (b) the general structure of a full-connected network, where regularization and dropout are used to prevent overfitting, and DIM represents the dimension of the tensor.神经网络结构示意图　(a) 卷积神经网络, INPUT表示输入层, Convolution表示卷积层, MAXPOOL表示池化层, Full connection表示全连接层, OUTPUT表示输出层, PADDING方式均为SAME; (b) 全连接网络的一般结构, 其中hidden layer表示隐藏层, 使用正则化和dropout来防止过拟合, DIM表示输入张量的维度

Fig. 2. Relationship between adsorption rate and temperature. Wherein the chain length N = 160, inset (a) is the conformation of polymer adsorbed on the surface at temperature T = 1.0, and inset (b) is the conformation of polymer desorbed from surface at temperature T = 2.0. 吸附率与温度之间的关系. 其中链长N = 160, 插图(a) 是温度T = 1.0时的吸附态构象, 插图(b) 是温度T = 2.0时的脱附态构象

Fig. 3. The relationship between the recognition rate and the Epochs of training case, the number of neural network layers and the number of training samples obtained from each temperature: (a) the plot of recognition rate versus Epochs. SPT (sample per temperature) represents the number of samples extracted at each temperature for training the neural network. The sample is labeled by status. Where n_h = 1 indicates that the number of hidden layers is equal to 1, and the others are similar. All of n_h = 1 to 3 uses a sample of SPT = 192 for training, and the remaining samples are used for verification. The illustration depicts the relationship between the recognition rate and the number of hidden layers, which is the final stable recognition result for each classifier; (b) the plot of the recognition rate versus the number of training samples selected at each temperature. The sample is marked by status and the number of hidden layers is equal to 3. The y-axis is the stable recognition rate of different number of training samples under a sufficiently large Epoch. The validation set is SPT = 7680 and is not repeated with the training set. 识别率与训练样本的Epoch, 神经网络层数以及每个温度采用的训练样本数之间的关系　(a) 识别率与Epoch的关系图, SPT (sample per temperature)表示在每个温度下抽取的用于训练神经网络的样本数目, 采用状态标记法标记样本. 其中n_h = 1表示隐藏层数目为1, 其他的类似, n_h = 1至 n_h = 3均采用SPT = 192的样本用于训练, 剩余的样本用于测试与验证, 插图描述的是识别率与隐藏层数目n_h的关系, 该识别率为每个学习器最终稳定的识别结果; (b) 识别率与每个温度采用训练样本数目的关系图, 采用状态标记法标记样本, 隐藏层数等于3, 纵坐标为不同训练样本在足够Epoch下达到稳定时的识别率, 测试集均为SPT = 7680, 且与训练集不重复

Fig. 4. A plot of the result based on the neural network. The x-axis is the temperature, State represents the probability that the sample at each temperature is recognized as a certain state, The letter S represents the state labeling method, the letter T represents the temperature labeling method, AD represents the adsorption state, and DE represents the desorption state. The figure shows the learning results of the two labeling methods. The recognition rate of the convolutional network is 98.3%, the AUC value is 0.9989, the fully connected network is 97.6%, the AUC value is 0.9982, and the critical phase transition temperature is 1.5 of the two labeling methods. 神经网络训练的识别结果图. 横坐标为温度, State表示每个温度下的样本被识别为某个状态的概率, S表示状态标记法, T表示温度标记法, AD表示吸附态, DE表示脱附态. 图为两种标记方法的识别结果, 卷积网络的识别率为98.3%, AUC值为0.9989, 全连接网络为97.6%, AUC值为0.9982, 两种标记方法的临界相变温度

Fig. 5. The schematic diagram of the adsorption rate of polymer adsorbed on the stripe surface changes with temperature and typical tri-state conformations: (a) the single-strip adsorption state, where the temperature is 0.3; (b) the multi-strip adsorption state, where the temperature is 0.9; (c) the desorption state, where the temperature is 3.0. Wherein the chain length N is 160, and the stripe width L of the adsorption surface is 4. The stripe direction is perpendicular to the x axis and extends along the y axis, and the selected space size is . For the adsorption surface, the dark part is the adsorption surface and the white part is the non-force surface. 高分子链在条纹表面的吸附率随温度的变化以及典型的三态构象示意图　(a) 单条纹吸附状态, 温度T = 0.3; (b) 多条纹吸附状态, T = 0.9; (c) 脱附状态, T = 3.0. 其中链长N = 160, 条纹宽度L = 4, 条纹方向垂直于x轴, 沿着y轴方向延伸, 选取的空间尺寸为 , 在条纹表面上, 深色部分为吸附条纹, 白色部分为作用力排斥条纹

Fig. 6. A plot of the result of the neural network training. The x-axis is the temperature, the State indicates the pro-bability that the sample at each temperature is recognized as a certain state, S indicates the state labeling method, T indicates the temperature labeling method, SS indicates the single-striped adsorption state, MS indicates the multi-striped adsorption state, and DE indicates desorbed state. The figure shows the learning results of two kinds of labeling methods. The recognition rate of convolutional network is 94.78%, where the AUC value is 0.9930. the fully connected network is 93.85%, where the AUC value is 0.9918, and the critical phase transition temperature of state labeling method is 0.55 and 1.1. The critical phase transition temperature of the temperature labeling method is 0.55 and 1.05. 神经网络训练的识别结果图　横坐标为温度, 纵坐标State表示每个温度下的样本被识别为某个状态的概率, 图标中S表示状态标记法, T表示温度标记法, SS表示单条纹吸附态, MS表示多条纹吸附态, DE表示脱附态. 其中卷积网络的识别率为94.78%, AUC值为0.9930, 全连接网络为93.85%, AUC值为0.9918, 状态标记法的临界相变温度 , 温度标记法的临界相变温度 ,

Fig. 7. The distribution of neural network learning results: (a) the distribution of learning outcomes on the homogeneous surface, green indicates that the correct sample, and other samples that identify the error; (b) the distribution of learning results on the pattern-stripe surface, blue indicates that the correct sample, and other samples that identify the error.神经网络学习结果的分布图　(a) 均质表面下的学习结果分布, 绿色表示识别正确的样本, 其他的表示识别错误的样本; (b) 条纹表面下的学习结果分布, 蓝色表示识别正确的样本, 其他的表示识别错误的样本