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Author(s):: WANG Xiangdong1; XU Pengcheng1; LU Tian1; LIU Xiujuan2; LU Wencong1; 2; (1. Materials Genome Institute，Shanghai University，Shanghai 200444，China) (2. College of Sciences，Shanghai University，Shanghai 200444，China)

Keywords:: pattern recognition; optimal projection recognition; ternary gold alloy materials; resistivity; machinelearning

Abstract:: Gold and its alloys, especially ternary gold alloys, have been widely used in the field of electrical contact materials. Due to the complicated components and ratios of ternary gold alloy, how to efficiently design electrical contact materials of ternary gold alloy with low resistivity is still a challenge. In this work, new ternary gold alloy materials with lower resistivity were designed based on the inverse design method combining qualitive method (optimal projection of pattern recognition) with quantitative method (XGBoost). The critical features were screened out by using the maximum relevant minimum redundancy (mRMR) integrated with the XGBoost algorithm. Three candidate samples with lower resistivity, i.e., AuZr1.95Cu0.52，AuZr1.12Cu4 and AuSc1.86Cu2.75 were designed by using the inverse projection of pattern recognition method developed in our laboratory, and the resistivity of the candidate samples was estimated by the XGBoost model. The results indicate that the predicted negative logarithms (-lg ρ) of designed samples are 6.718, 6.707 and 6.701, respectively, exceeding the maximum value of 6.68 in the original data set. As a reference method for material inverse design, this research method is helpful for mining the statistical regularities in experimental data, and can accelerate the design of new materials.