Density prediction of liquid alkali metals and their mixtures using an artificial neural network method over the whole liquid range

• Density of alkali metals and their mixtures have been estimated using an ANN model.
• Tansig-tansig architecture with 15 neurons in hidden layer was used to design the ANN model.
• The AAD% for train, validation, and test sets are 0.1029, 0.1396 and 0.1002, respectively.
• The advantage of this technique is its high speed, simplicity and generalization.
• A comparison between this method and some previous works has been made.

In this study, the application of artificial neural network (ANN) method in predicting the density of alkali metals and their mixtures is investigated. A total number of 595 different data points of these compounds were used to train, validate and test the model. A typical three-layer feedforward backpropagation neural network has been trained by the Levenberg Marquardt algorithm. The tansig-tansig transfer functions with 15 neurons in the hidden layer makes the least error, so a network with (8-15-1) structure was used to design the ANN model. The average relative deviations for train, validation, and test sets are 0.1029, 0.1396, and 0.1002, respectively. A comparison between our results and those obtained from some previous works shows that this work, as an excellent alternative, can provide a simple procedure to predict the density of these compounds in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions.