In the present study, a layer type neural network computation was applied to estimate the standard entropy of binary solid oxides, sulfides and halides. Independent variables
to influence the thermodynamics property associated with dispersion or randomness in the crystals were used as input parameters for the calculation. 325 substances involving 12
input parameters were applied to the calculation. The regression computation enabled reproduction of training data cited in learning process and prediction of test data not used in
the learning process with high accuracy.