Authors: Murad Ali Khan | Syed Shehryar Ali Naqvi | Muhammad Faseeh | Do-Hyeun Kim
Source: Journal of Alloys and Compounds (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Cross-material inverse learning and knowledge transfer for property-driven design of advanced ceramics In the rapidly advancing field of materials science, artificial intelligence (AI) has emerged as a powerful tool for enhancing material property prediction, inverse design, and process optimization, particularly for complex ceramic systems. This study proposes a unified AI-driven framework that integrates decision-tree-based imputation, generative data augmentation, and advanced learning paradigms for accurate component and process recommendation. Initially, CART-based imputation is employed to robustly handle missing data, establishing a reliable baseline across KNN, BaTiO 3 , and PZT ceramic datasets. To further enhance data diversity and generalization, Variational Autoencoder (VAE) augmentation is applied, resulting in consistent performance gains across all materials. Building upon this enriched data, a Transformer-based inverse learning model is developed to infer material components and processing parameters from target properties. Extensive experiments demonstrate systematic performance improvements at each stage of the framework. The proposed approach achieves R 2 scores of 0.930, 0.959, and 0.928 after augmentation for KNN, BaTiO 3 , and PZT, respectively, while multi-material joint learning further improves predictive accuracy to an R 2 score of 0.987. Moreover, cross-material transfer learning enables effective knowledge adaptation between ceramic systems, achieving R 2 scores up to 0.986, demonstrating strong generalization in data-scarce scenarios. Inverse design evaluations further confirm low reconstruction errors, validating the reliability of the proposed framework. Overall, the results highlight the effectiveness of combining structured preprocessing, generative augmentation, joint learning, and transfer learning within a Transformer-based inverse modeling pipeline. This work underscores the potential of advanced AI techniques to accelerate data-driven materials design and provides a scalable foundation for future intelligent materials engineering applications. • A comprehensive AI-driven framework for ceramic materials that integrates CART-based imputation, VAE-based augmentation, Transformer-based inverse learning, multi-material joint learning, and cross-material transfer learning. • Systematic demonstration that CART imputation outperforms conventional methods by preserving conditional data structure and improving baseline R2 performance across all material systems. • Extensive augmentation analysis showing that VAE-based augmentation consistently outperforms GAN and WGAN-GP, leading to substantial improvements in RMSE and R2 scores. • Development of a Transformer-based inverse learning model that achieves near-perfect predictive accuracy for multi-material joint learning (R2 = 0.987) and strong cross-material transfer performance. • Validation of inverse property-driven material design through low-error inverse predictions across KNN, BaTiO3, and PZT systems. Transfer of learning ; Inverse ; Component (thermodynamics) ; Reliability (semiconductor) ; Generalization ; Process (computing) ; Autoencoder ; Field (mathematics) ; Generative grammar Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies