Microstructure evolution in service significantly influences the properties of advanced materials. Numerical simulation can effectively capture microstructure development and provide abundant high-fidelity data. However, effective 3D microstructure-informed property prediction methods are lacking due to the complexity and richness of 3D microstructural data. In this work, a novel approach combining phase-field simulation and a 3D convolutional neural network is proposed to explore the compositio