Engineering materials microstructure has been a critical step in developing novel materials with desired properties. Specifically in the field of electronic devices microstructure engineering is pivotal. It influences properties like conductivity, carrier mobility, and band structure in semiconductors, thin film electronics, and photovoltaics. Tailoring microstructures enhances efficiency and functionality, underpinning advancements in semiconductor technology, renewable energy, and emerging electronic applications. Traditionally, forward-based models, comprising experimental and high-fidelity approaches, have been employed in the engineering of specific microstructures. These models often rely on trial-and-error methodologies to achieve desired structural configurations. In this presentation, we introduce a novel fused data deep learning framework facilitating materials microstructure engineering through inverse design. Using a case study focused on high-entropy permanent magnets, we underscore the significance of microstructure engineering. Furthermore, we validate the model's efficacy. specifically, for FeCrCo-based permanent magnets.
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