Generative AI inversely designs amorphous alloys with customized properties

In this study, we propose a framework for the inverse design of amorphous alloys based on multi-objective properties, which not only constructs the potential relationship between material properties and composition, but also verifies the inherent constraints between different objective properties. The framework uses the variational autoencoder and conditional variational autoencoder as the core generation model, and combines the grid filter, quantum network, and machine learning algorithm to filter and recommend the generated data, thereby improving the feasibility of practical applications. To validate and analyze the performance of the proposed framework, we implemented an amorphous alloy design scheme covering three key properties: saturation magnetic strength, coercivity, and glass transition temperature. The experimental results show that the framework can generate alloy compositions that are extremely similar to the experimental data under the same property conditions, thus validating its excellent generation capability. More importantly, our material inverse design framework has good scalability. This allows it to flexibly respond to the increasingly complex and specific property requirements of various materials in the future.