TY - JOUR
T1 - Interpreting Materials Data with Artificial Intelligence: From Prediction to Scientific Understanding
AU - Fatima Zahra Amrani
AU - Youssef Benali
AU - Samira El-Haddad
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2024
VL - 3
IS - 1
DO - 10.68159/z805552631
SP - 42
N2 - The integration of artificial intelligence (AI) and machine learning (ML) into materials science has fundamentally transformed how material properties are predicted, analyzed, and understood. While early data-driven approaches emphasized predictive accuracy and high-throughput screening, recent advances are increasingly focusing on interpretability and explainability, enabling AI models to contribute to mechanistic scientific insight rather than functioning as opaque black boxes. This study examines the evolution of interpretable AI in materials science and highlights the transition from property prediction to explanation-driven understanding of structure–property relationships. In this thesis, we investigate the progress in machine learning frameworks that operate with limited or implicit structural information, alongside the growing use of explainable AI (XAI) techniques to uncover physically meaningful descriptors, atomic-scale interactions, and microstructural drivers of material behavior. Methods such as graph-based learning, attention mechanisms, feature attribution, and uncertainty-aware modeling are discussed for their ability to improve model reliability, expose data bias, and guide hypothesis generation. Representative applications across alloys, perovskites, organic semiconductors, and ferroelectric materials demonstrate how interpretable models have revealed governing mechanisms spanning atomic, mesoscopic, and macroscopic length scales. Beyond individual case studies, this study examines persistent challenges in interpretable materials AI, including data quality, generalizability, explanation stability, and computational overhead. We argue that interpretability is not merely an auxiliary feature but a prerequisite for trustworthy and scientifically helpful AI in materials research. By synthesizing recent methodological and application-driven advances, this review positions interpretable AI as a critical enabler of mechanism-oriented discovery, experimental validation, and theory development, ultimately advancing AI from a predictive accelerator to an integral partner in scientific understanding.
UR - https://iamrp.net/z805552631
ER -