为了使用分子相互作用体积模型（molecular interaction volume model，MIVM）准确便捷预测出合金溶液中组元的活度，建立了活度预测的BP（back propagation）神经网络模型和算法，模型的输入层为合金溶液中组元的实验活度系数，输出层为分子对位能相互作用参数，隐含层设定为一层。采用遗传算法优化BP神经网络模型各结构参数，在遗传算法中使用合金溶液中组元的无限稀活度系数的实验值和理论值的偏差作为适应度函数，以偏差最小为目标进行优化以保证BP神经网络的有效性。最后以Pb-Bi，Sn-Bi，Sn-Pb，Fe-Cu二元合金溶液中组元活度预测为例对BP神经网络模型和算法进行验证。结果表明：组元活度预测值与实验值之间的平均相对误差均小于4%，绝对偏差小于0.78，能满足工程计算要求。
A back propagation (BP) algorithm of the neural network was established so as to use Molecular Interaction Volume Model (MIVM) to predict the components activity in alloy solution accurately and conveniently. Its input layer is the experimental activity coefficient of components in alloy solution, output layers are molecular pair energy interaction parameters and hidden layer is set to one layer. The structural parameters in BP neural network model were optimized via genetic algorithms, where the deviation between the experimental and theoretical values of the infinite dilution activity coefficient as fitness function. The optimization was carried out with minimum deviation so as to ensure the validity of BP neural network. Finally, the BP neural network model and algorithm were validated by taking the binary alloy solutions of Pb-Bi, Sn-Bi, Sn-Pb and Fe-Cu as examples. The results show that the average relative errors between the predicted and experimental values of components activity in the alloys are less than 4% and the absolute deviation less than 0.78, which can meet the requirements of engineering calculation.