Assessing stability remains a fundamental prerequisite for deploying materials across a wide range of applications, including batteries, catalysts, and photovoltaics. However, first-principles stability checks such as phonon dispersion and energy above hull calculations typically require days to weeks of computing time per composition, creating a critical bottleneck for truly high-throughput discovery. In this Perspective, we highlight the underutilized potential of geometric tolerance factors (T_f) as lightweight yet informative indicators for rapid stability assessment. First, we review the T_f developed for representative materials systems, including perovskites, spinels, and garnets, and analyze recent cases where such indicators have been integrated into AI-driven materials discovery. Then, we identify key open challenges in designing T_f that are both accurate and generalizable, as well as in effectively incorporating them into AI frameworks. The potential solutions, including active learning for multi-composition structure, electron density profile-based learning for ionic radii estimation, and diffusion model for thermodynamic and kinetic stability, are proposed to address these challenges. The synergy between T_f-based heuristics and advanced AI models has the potential to triage vast compositional spaces before committing to expensive first-principles stability validation, thereby enabling broader innovations in materials design and deployment.

Graph neural networks (GNNs) have become a transformative modeling paradigm in materials science, offering a data-efficient and structure-aware approach for learning from complex material systems. This review focuses on the recent progress of GNNs in alloy design and property prediction. We begin by introducing the foundational concepts of graph representations and the general architecture of GNNs, including node embeddings, message passing, and pooling strategies. The review then categorizes major types of GNNs, including supervised and unsupervised learning, with a focus on the achievements and applications of GNNs in materials modeling, and discusses their strengths and inherent limitations in the context of materials modeling. Particular emphasis is placed on the application of GNNs in the alloy domain, covering a diverse range of data types, from atomic structures and compositions to microstructural images, and target properties, such as mechanical strength, thermal stability, and phase stability. We highlight how GNNs are integrated into alloy composition optimization, multi-property prediction, and frontier research workflows. The review concludes with a summary of multi-model and multiscale approaches and outlines key challenges and future directions for constructing generalizable, physics-informed GNN frameworks for alloy discovery.

Phase-change materials (PCMs) are among the most promising candidates for next-generation non-volatile memory and neuromorphic computing technologies. However, their photonic applications are hindered by a trade-off between refractive index contrast and optical absorption losses. Artificial intelligence-assisted computational approaches are essential for fundamental understanding and device modeling of PCMs. In this work, we systematically investigate structural and optical properties of crystalline and amorphous Ge₂Sb₂Se_xTe_5-x (x = 0 to 4) alloys using density functional theory (DFT), and then use the DFT-computed optical parameters for modeling and optimization of photonic computing devices via the finite-difference time-domain method. Among the investigated compositions, we identify a promising candidate, i.e., Ge₂Sb₂Se₃Te₂ for all-optical switching on a silicon-on-insulator (SOI) platform. Finally, we design a dual-disk PCM waveguide structure on SOI with an enhanced switching contrast and a low optical loss for scalable photonic neural network application.

Uncertainty is crucial when the available data for building a predictor are insufficient, which is ubiquitous in machine-learning-driven materials studies. However, the impact of uncertainty estimation on predictor selection and materials optimization remains incompletely understood. Here, we demonstrate that in active learning, uncertainty estimation significantly influences predictor selection, as well as that the calibration of uncertainty estimation can improve the optimization. The idea is validated on three alloy datasets (Ni-based, Fe-based, and Ti-based) using three commonly used algorithms - support vector regression (SVR), neural networks (NN), and extreme gradient boosting (XGBoost) - which yield comparable predictive accuracy. It is shown that XGBoost presents more reliable uncertainty estimation than SVR and NN. Using the directly estimated uncertainty for the three predictors with similar accuracy, we find that the optimization is quite different. This suggests that uncertainty estimation plays a role in predictor selection. The uncertainty estimation is then calibrated to improve reliability, and its effect on optimization is compared with the uncalibrated case. Among the nine cases considered (three models and three datasets), eight show improved optimization when calibrated uncertainty estimation is used. This work suggests that uncertainty estimation and its calibration deserve greater attention in active learning-driven materials discovery.

Co-based superalloys exhibit exceptional high-temperature properties, granting them broad application prospects in the superalloy domain. However, constrained by the exorbitant trial-and-error costs and protracted research cycles inherent in their development, machine learning (ML) has emerged as the most pivotal research direction in this field. This review systematically examines ML-driven approaches for Co-based superalloys, progressing from fundamental regression models for property prediction to advanced multi-model, multi-scale computational paradigms-structured according to model sophistication and problem complexity. Furthermore, we discuss current challenges and future prospects in applying ML to Co-based superalloys, with particular emphasis on addressing data scarcity through the integration of high-throughput experimentation. This synergistic approach enables efficient establishment of standardized superalloy databases, accelerating research progress to meet evolving demands in aerospace applications.