Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys

Abstract

In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties, a predictor−corrector inverse design scheme (PCIDS) consisting of a predictor module and a corrector module was presented. A high-precision forward prediction model based on deep neural networks was developed to implement these two parts. Of utmost importance, domain knowledge-guided inverse design networks (DKIDNs) and regular inverse design networks (RIDNs) were also developed. The forward prediction model possesses a coefficient of determination (R²) of 0.990 for the shear modulus and 0.986 for the bulk modulus on the testing set. Furthermore, the DKIDNs model exhibits superior performance compared to the RIDNs model. It is finally demonstrated that PCIDS can efficiently predict amorphous alloy compositions with the required target properties.