TY - JOUR
T1 - Graph Neural Networks for Materials Property Prediction: A Decadal Review of Advances and Limits
AU - Daniel Fischer
AU - Laura Meier
AU - Thomas Braun
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2022
VL - 1
IS - 1
SP - 84
N2 - The advent of graph neural networks (GNNs) has revolutionized computational materials engineering by enabling sophisticated representations of atomic structures and interactions for property prediction. This review synthesizes key developments in GNN architectures tailored for materials science, focusing on their application in predicting mechanical, electronic, and thermodynamic properties of diverse materials systems, including polycrystals, metal-organic frameworks, and perovskites. Drawing from high-impact studies, we examine the evolution from basic crystal graph convolutional networks to advanced variants incorporating transfer learning, data augmentation, and force field integration. The synthesis highlights how GNNs address challenges in materials data sparsity and structural complexity through graph-based featurization, leading to improved accuracy in property forecasts compared to traditional machine learning methods. We integrate perspectives on GNNs' role in broader data-driven ecosystems, including their synergy with active learning for autonomous discovery pipelines. Limitations such as interpretability and scalability are critically assessed, alongside advances in benchmark frameworks that standardize evaluations. The review positions GNNs as a cornerstone of next-generation materials informatics, accelerating the design of high-performance materials for energy, catalysis, and structural applications. Future outlooks emphasize hybrid integrations with physics-based simulations to bridge experimental and computational gaps, fostering closed-loop systems for rapid materials innovation. This narrative underscores the transformative potential of GNNs in reshaping materials engineering paradigms.
UR - https://iamrp.net/e795301159
ER -