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用化学结构参数- 模式识别人工神经网络方法对非晶态合金系的磁性、超导电性和力学性能( 包括饱和磁致伸缩系数、饱和磁感应强度、矫顽力、超导转变温度、硬度和抗拉强度) 与组成结构之间的关系进行了定性分析和定量计算,采用的化学结构参数为平均价电子数、混合熵、原子半径比、电负性差、功函数差和电子密度差等.结果表明,定性分析结果与实验结论一致,定量计算结果与实验测定值符合较好.
The magnetic, superconductivity and mechanical properties (including saturation magnetostriction coefficient, saturation magnetic flux density, coercivity, superconducting transition temperature, hardness and magnetic properties) of amorphous alloys were studied by using chemical structure parameter- pattern recognition artificial neural network Tensile strength) and the composition of the structure were qualitative analysis and quantitative calculation, the use of chemical structure parameters for the average number of valence electrons, mixed entropy, atomic radius ratio, negative electronegativity, work function difference and electron density difference and so on. The results show that the qualitative analysis results agree well with the experimental results, and the quantitative calculation results are in good agreement with the experimental ones.