## 3 Materials and Methods
### 1D-CNN-GRU Architecture  
To construct the unified neural network, the network consists of 12 layers: three 1D convolutional neural network (1D-CNN) layers, two Gated Recurrent Unit (GRU) [32] levels, two dropout layers, one maxpooling, one flatten layer, and three fully connected layers. The signals are initially routed via the first convolutional layer. The kernel utilized in the first layer of our proposed 1D-Convolutional GRU network is set to 2, which indicates that all weights will be shared by every stride of the input layer of atom index 0 and atom index 1, as well as the scalar coupling constant outputs. In the kernel window, input values multiplied by weights are added together and utilized to build the feature map value. The first convolution layer's output is 2 \(\times\) 128, but it is also the input to the following convolutional layer. The filter size is lowered to 64 in the second 1D-convolutional layer, from 128 in the first, and the kernel size is adjusted to 3 for all 1D convolutional layers. In addition, the input size in the third 1D-convolutional layer is 2 \(\times\) 64, with padding set to the same value in all three 1D-convolutional layers. Figure 2: Schematic diagram of the one-dimensional convolutional gated recurrent neural network. The mathematical expression of the 1D-convolutional layer is as follows:  
\[y=\beta\Big{(}\sum_{i=1}^{n}conv1D(Wx_{i})+b_{i}\Big{)} \tag{2}\]  
where \(\beta()\) represents the Leaky ReLU (Rectified Linear Unit) activation function, \(n\) represents the number of feature maps in the layer, \(conv1D\) represents the one-dimensional convolutional neural network operation with same padding, \(W\) represents the trainable one-dimensional convolutional kernel, \(x_{i}\) represent \(i\)th feature map, and \(b_{i}\) represents the bias of the \(i\)the feature map. In all 1D-convolutional neural networks, the feature maps are subsequently input into the activation leaky ReLU function. The following is the mathematical equation for Leaky ReLU:  
\[\beta()=\{\beta x,\textit{for }0<x\leq 0\} \tag{3}\]  
Layer 4 uses maxpooling to assist downsampling the input representation, by taking the maximum value over a spatial window of size, with hyperparameters such as pool size set to 3, padding set to \"same\", and strides set to 2. The maxpooling can be expressed as:  
\[mp^{e}=\max(mp^{e1}:e\leq e1<e+s) \tag{4}\]  
where \(mp^{e1}\) is the \(e1\) neuron before maxpooling operation, \(mp^{e}\) is the \(e\) neuron after max-pooling operation, and \(s\) is the size of the pooling. A gated recurrent unit (GRU) was implemented in the fifth layer with 128 neurons to overcome the vanishing gradient problem of 1D-CNN, and the activity regularizer of layer 2 was adjusted to 0.001, to penalize the layer output. Update and reset gates are the two gates used by GRU. These two gates determine which data are sent to the output. The network unit plugs in the update gate with input \(x_{t}\) and multiplies it by its own weight \(W(z)\). The same is true for \(h_{t-1}\), which stores data from earlier (\(t-1\)) units and multiplies it by its own weight \(U(z)\). Both results were combined together, and the result was squashed using a Leaky ReLU activation function with an alpha of 0.001. The update gate assists the model in determining how much past data should be passed on to the future. The mathematical expression of the update gates is:  
\[z_{t}=\beta(W^{z}x_{t}+U^{z}h_{t-1}) \tag{5}\]  
where \(z_{t}\) is the update gate at time step \(t\), \(x_{t}\) is the current sample input at time step \(t\), \(W^{z}\) is the weight of the update gate, \(h_{t-1}\) is the previous hidden state at time step \(t\), and \(U^{Z}\) is the weight of the update gate. In the GRU model, a reset gate is utilized to determine how much information from the past should be forgotten. This formula is identical to the one used for the update gate. The weights and gates are what make the difference.