Editorial: Digital technology for materials science and processes modelling

In the age of artificial intelligence (AI), digital technology has emerged as an indispensable enabler of steel production, from research and development to industrial application. A concerted focus on the deployment of digital tools across the steel sector is urgently needed, not only to enhance efficiency and product quality but also to foster innovation in steel materials themselves. Such an approach is essential for adapting to dynamic market demands, driving technological progress, reducing resource consumption, and mitigating environmental impact. To highlight advances in this rapidly evolving field, we launch a special column on digitalization, dedicated to publishing the latest research findings, in-depth case studies, and comprehensive reviews on the implementation of digital technologies in the steel industry.Under this Research Topic, novel developments and state-of-the-art digital technologies in the field of steel materials are presented, with a special focus on fundamental theories of material digitization, general-purpose digital applications, design and optimization of material composition and processes. steel production, namely material computation, simulation modeling, AI-enabled databases, digital performance characterization, high-throughput design, and intelligent manufacturing. Zong et al. (2026) repurposed the primary scope of this study, shifting its focus toward addressing critical challenges in advancing steel material digitization, facilitating knowledge exchange, and embedding digital tools throughout the material development chain. By investigating these aspects, this research seeks to enable more efficient, economical, and intelligent approaches for steel material digitization, thereby facilitating new discoveries and breakthroughs.Although high-throughput computing has accelerated digitized material design and performance prediction by enabling large-scale simulations and data-driven models, conventional approaches still face limitations in generalization, domain knowledge integration, and structural optimization. To address these challenges, The contributions in this special column underscore three converging directions for digital technologies in materials science and process modelling: digitized material design (integrating high-throughput computing, physics-informed machine learning, and generative optimization for accurate cross-scale material discovery); intelligent manufacturing (leveraging AI-enabled databases, simulation modeling, and adaptive optimization to enhance efficiency, quality, and resource utilization in the steel industry); and digital detection (employing non-contact vision-based methods and hybrid multiscale modeling for low-cost, real-time property prediction and quality assessment). Covering fundamental theories, modeling frameworks, intelligent manufacturing, and digital detection, this column provides theoretical and practical guidance for digitizing steel and broader material systems. As the industry moves toward AI-driven smart manufacturing, the frameworks presented here offer pathways to develop digital solutions that are high-performing, scalable, cost-effective, and sustainable.NZ: Writing -original draft, TJ: Writing-review and editing.