Student at Yangzhou university
Xu Qin specializes in machine learning-assisted design of light alloys, with a focus on magnesium-based materials. Their research integrates data-driven models to predict material properties and develop innovative alloy compositions. Through generative modeling, feature fusion techniques, and interpretable AI, they have addressed challenges in microstructure-property relationships. Xu Qin’s work bridges the gap between computational predictions and experimental validations, delivering high-performance materials. The consistent high accuracy of their models has contributed to significant advancements in alloy design, enabling improvements in strength, ductility, and formability for various engineering applications.
Professional Profile
Scopus
Education
Xu Qin has pursued higher education in engineering and materials science, developing a strong foundation for research in alloy design. Their academic background combines mechanical engineering and advanced materials science, supporting interdisciplinary research capabilities. With formal training in both theoretical and practical aspects, they are adept at integrating computational models with experimental methods. Xu Qin’s education path has been aligned with their interest in applying machine learning to materials development, equipping them with the skills to conduct innovative, high-impact research that addresses complex industrial and academic challenges.
Professional Experience
Xu Qin’s professional research experience includes significant contributions to national and provincial-level projects in China. They have served as a key researcher in studies funded by the National Natural Science Foundation, focusing on generative models for alloy composition and microstructure prediction. As a principal researcher in a provincial innovation project, Xu Qin developed novel machine learning models to predict and enhance mechanical properties of magnesium alloys. Their roles have involved designing algorithms, processing large datasets, and integrating experimental results, thereby contributing to both academic knowledge and practical engineering solutions.
Research Interest
Xu Qin’s research interests revolve around the intersection of machine learning and materials science, particularly in magnesium and light alloys. They focus on developing predictive models for alloy composition, microstructure, and mechanical properties, using advanced AI architectures such as convolutional neural networks and generative adversarial networks. A key interest lies in interpretable AI, enabling insights into property-structure relationships. By integrating image-based analysis, statistical modeling, and knowledge graph approaches, Xu Qin aims to advance the predictive design of high-performance, sustainable alloys for applications in transportation, aerospace, and manufacturing industries.
Award And Honor
Xu Qin has been recognized with multiple prestigious awards, reflecting both academic excellence and research impact. They received the National Scholarship Award in 2023–2024, acknowledging outstanding performance at the graduate level. Additional honors include the Special Scholarship Award on two occasions and the Three Excellences Student Award twice, recognizing consistent achievement during both undergraduate and graduate studies. The China Railway Fourth Bureau Enterprise Scholarship further highlights Xu Qin’s capability to meet high professional standards, as it is awarded for strong academic merit, research innovation, and potential contributions to engineering fields.
Research Skill
Xu Qin possesses a robust skill set spanning computational modeling, programming, and experimental characterization. They are proficient in Python, Matlab, and various engineering software, enabling efficient data processing, simulation, and algorithm development. Experimental expertise includes EBSD, XRD, SEM, and TEM techniques, essential for microstructural analysis. Xu Qin also demonstrates strong capabilities in combining multiple data modalities for predictive modeling, supported by effective communication skills in English. This diverse technical and analytical proficiency ensures their ability to bridge theory and practice in the research and development of advanced alloys.
Publications
Xu Qin has authored and co-authored numerous publications in high-impact journals, contributing significantly to materials informatics and alloy design. Their work includes studies on generative models, feature fusion networks, and interpretable AI for magnesium alloys, with multiple papers published in Q1-ranked journals. Research outputs demonstrate expertise in applying computational methods to predict and optimize mechanical properties. These publications not only highlight technical achievements but also establish Xu Qin as an emerging scholar whose contributions advance the integration of machine learning into materials science for innovative industrial applications.
Title: PCS: Property-composition-structure chain in Mg-Nd alloys through integrating sigmoid fitting and conditional generative adversarial network modeling
Journal: Scripta Materialia, 2025
Conclusion
Xu Qin’s academic journey reflects a consistent dedication to advancing materials science through computational intelligence. Their contributions demonstrate the value of integrating machine learning with experimental research to accelerate alloy design. With a strong record of publications, prestigious awards, and versatile research skills, Xu Qin stands out as a promising researcher in the field. Their work addresses both scientific and industrial challenges, paving the way for the development of high-performance, sustainable materials. Xu Qin’s expertise positions them to make substantial future contributions to advanced engineering materials research.