The proposed framework can be extended to handle multiple target properties by modifying the disentangled variational autoencoder (VAE) architecture to accommodate a multi-objective optimization approach. This can be achieved through the following strategies:
Multi-Head Output Layer: The recognition model can be designed with multiple output heads, each corresponding to a different target property, such as mechanical strength and corrosion resistance. Each head would learn to predict its respective property based on the shared latent representation, allowing the model to capture the interdependencies between different properties.
Joint Loss Function: The training process can incorporate a joint loss function that combines the individual losses from each target property. This would ensure that the model learns to optimize for all properties simultaneously, balancing the trade-offs between them. For instance, a weighted sum of the losses can be used, where the weights reflect the importance of each property in the context of the specific materials design task.
Hierarchical Latent Space: A hierarchical latent space can be introduced, where the first layer captures general features relevant to all target properties, while subsequent layers capture property-specific features. This structure allows for a more nuanced representation of the materials, facilitating the disentanglement of complex relationships between multiple properties.
Expert-Informed Priors: By leveraging expert-informed prior distributions for each target property, the model can be guided to explore the latent space more effectively. This can enhance the robustness of the predictions, especially when dealing with limited labeled data for certain properties.
Post-Hoc Analysis for Multi-Property Insights: Utilizing post-hoc analysis techniques, such as SHAP values, can provide insights into how different features contribute to each target property. This interpretability can help in understanding the trade-offs involved in optimizing multiple properties and guide the design process.
By implementing these strategies, the framework can effectively manage the complexities of multi-property optimization in inverse materials design, leading to the discovery of materials that meet diverse performance criteria.