Calculation of Standard Entropy of Binary Solid Compounds by Neural Network Computation

Abstract

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.