Fig. 2: ML parameter optimization.
From: Deep learning model to predict fracture mechanisms of graphene
The ML model consists of multiple layers including a two convolutional layers to learn geometric features of crack slices, an LSTM layer to learn sequential relations between them, and a dense layer to classify the results. Each MD graphene fracture image is sliced into ML training data by b eight different combinations of input and output widths to yield eight sets of ML-predicted fracture paths. The set of parameters yielding predictions closest to MD fracture results is input width 32 output width 2. Comparing the c training and validation loss of the model shows close agreement without overfitting and a model accuracy near 95%.
