12/12/2023 0 Comments Lattice pattern cnn![]() ![]() With growing experimental and simulated dataset size for materials science research, 2–5 the ability of algorithms to automatically learn and improve from data becomes increasingly useful. 1 Then these patterns can be used to do data classification or value prediction. Introduction Machine learning is concerned with the automatic discovery of patterns in data through the use of computer algorithms. ![]() Using compounds with formula X 2YZ in the Inorganic Crystal Structure Database (ICSD) as a second training set, the stability of full-Heusler compounds was predicted by using the fine-tuned CNN, and tungsten containing compounds were identified as rarely reported but potentially stable compounds. Transfer learning was then utilized by fine-tuning the weights previously initialized on the OQMD training set. ![]() The mean prediction errors were within DFT precision, and the results were much better than those obtained using only Mendeleev numbers or a random-element-positioning table, indicating that the two-dimensional inner structure of the periodic table was learned by the CNN as useful chemical information. Using the periodic table as representation, and full-Heusler compounds in the Open Quantum Materials Database (OQMD) as training and test samples, a multi-task CNN was trained to output the lattice parameter and enthalpy of formation simultaneously. Here we show that CNNs can learn the inner structure and chemical information in the periodic table. In recent years, convolutional neural networks (CNNs) have achieved great success in image recognition and shown powerful feature extraction ability. ![]()
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