Where TP, TN, FP, FN stands for true positive, true negative, false positive, and false negative respectively. Multiple incorrect identifications leading to false positives contribute to reduced precision while failing to identify true positives results in a decreased recall. The F1 score serves as a means to strike a balance between these two metrics and is consequently employed for the purpose of optimizing the hyperparameters of the Autoencoder. The ANN Autoencoder is shown in Figure . The left side(encoder) of the network encodes the input data X into a latent dimension (central layer), while the right(decoder) side of the network aims to reconstruct the input X'. In an ideal scenario the input passed through the Autoencoder is perfectly reconstructed(X=X'). However, information is lost during the encoding and decoding process. This results in a different X and X', using which a reconstruction error is calculated. This reconstruction error is the anomaly score we discussed earlier. It reveals how the data is represented in the latent layers and is widely used for pattern recognition. The autoencoder is trained using a combination of crystal properties and elemental properties. These elemental descriptors are represented using the mathematical expressions average, minimum, maximum, and difference respectively. Out of the total 170 features, we ended up taking 60 features. The process of choosing the features is described in the result section. These 60 descriptors are provided as input to the Autoencoder in figure1, which compresses it to the latent dimension using 2 fully connected hidden layers. The ReLU (Rectified Linear Unit) activation function is applied at each synaptic connection to introduce non-linear properties within the neural network. The decoder network uses a ReLU for the hidden layer and a sigmoid for the output layer. The autoencoder network is trained to minimize the loss using ADAM 30 , a stochastic gradient descent method. The loss in this case is defined as the mean squared error between the input X and output X'. Usually, the network is optimized by minimizing this loss to get better reconstructions of the input. However, for Anomaly detection this is not necessary therefore we optimize the hyperparameters and the network architecture by maximizing the F1 score.