Fig. 1: The workflow of our works.
From: A general and transferable deep learning framework for predicting phase formation in materials
a The workflow of the proposed GTDL framework (in green solid arrows) and conventional machine learning (in black dotted arrows) which does not have the ability of automatically extracting features and knowledge transfer. The schematics for assembling dataset, data representation, machine learning, knowledge transfer, and an example of PTR (periodic table representation) were given. MF, SNN, RF, SVM, and CNN denotes manual features, shallow neural network, random forest, supported vector machine, and convolutional neural network, respectively. In GTDL framework, raw data are mapped to 2-D pseudo-images first, features are then extracted automatically by convolutional layers, knowledge is transferred by sharing the well-trained feature extractors for new tasks with small dataset. b The schematics for our VGG-like convolutional neural network.
