In order to find suitable materials for specific application scenarios or feature requirements,a reverse design framework based on the Cycle Generative Adversarial Network(Cycle-GAN)was proposed,which integrates Long Short-Term Memory(LSTM)artificial neural networks and Variational Autoencoders(VAE)and applied to the reverse design of magnesium alloys,covering aspects from mechanical performance to composition and extrusion parameters.Compared to traditional artificial neural networks and support vector machine algorithms,the framework model demonstrates an improvement of 27％and 47％in prediction accuracy,respectively.The mean squared error and mean absolute error on the test dataset are 0.09 and 0.15.Additionally,the framework provides a reference range,narrowing down the everse design space for of magnesium alloys.
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