Associate Professor at Taiyuan University of Technology, China
Dr. Liu Xiaoda is an Associate Professor and Master’s Supervisor at the School of Artificial Intelligence, Taiyuan University of Technology (TYUT). Born in February 1982 in Houma, Shanxi Province, he has devoted his academic career to advancing research in material gene engineering and machine learning. With a strong foundation in Materials Physics and Chemistry, Dr. Liu has developed a robust interdisciplinary profile blending materials science with artificial intelligence. A member of the Jiusan Society and a Han ethnic group representative, he actively contributes to both scientific and civic communities. Over the years, he has earned a reputation for excellence in teaching, mentorship, and research. He is also an active member of prominent provincial-level research centers, including the Shanxi Provincial Key Laboratory of Data Governance and Intelligent Decision-Making and the Shanxi Provincial Key Laboratory of Metal Materials and Corrosion Control. A member of the China Computer Federation (CCF), Dr. Liu plays a critical role in the convergence of computer science and materials engineering. With his multifaceted expertise and forward-looking vision, Dr. Liu is committed to pushing the boundaries of intelligent materials design and digital transformation in materials research.
Professional Profile
Education
Dr. Liu Xiaoda received his doctoral degree from Taiyuan University of Technology, where he majored in Materials Physics and Chemistry. This rigorous academic training equipped him with comprehensive knowledge in the fields of materials science, computational modeling, and experimental analysis. During his Ph.D. journey, Dr. Liu focused on exploring novel methods to optimize material structures using data-driven approaches, laying the foundation for his later work in materials informatics. His academic background provided him not only with deep theoretical insights into materials behavior at the micro and nanoscale but also with practical skills in handling complex datasets, laboratory instrumentation, and simulation software. Dr. Liu’s multidisciplinary education fostered his passion for integrating artificial intelligence techniques with materials science—a relatively novel and promising frontier. The strong academic environment at Taiyuan University of Technology, known for its emphasis on research and innovation, nurtured Dr. Liu’s scholarly abilities and prepared him to tackle complex scientific challenges. Throughout his academic life, he has remained committed to lifelong learning, regularly attending workshops and technical training to stay updated on emerging trends in machine learning, computational chemistry, and big data analytics.
Professional Experience
Dr. Liu Xiaoda has accumulated extensive professional experience through his academic appointments at Taiyuan University of Technology. From 2019 to 2021, he served as a Lecturer at the School of Big Data, where he began developing interdisciplinary research on materials data science. His dedication and innovative approach led to his promotion to Associate Professor in 2022 at the School of Computer Science and Technology (formerly also the School of Big Data). In 2024, Dr. Liu transitioned to the School of Artificial Intelligence, continuing his role as an Associate Professor and broadening the impact of his research in machine learning-driven materials engineering. As a Master’s Supervisor, he has mentored numerous graduate students, guiding them through advanced topics in smart materials, data mining, and artificial intelligence applications in physical sciences. His academic responsibilities also include curriculum development, research supervision, and collaborative project leadership. Dr. Liu’s multidisciplinary insight enables him to lead initiatives that bridge traditional materials science with cutting-edge computational techniques. His commitment to academic excellence is further evidenced by his leadership roles in key research projects funded by the Shanxi Provincial Government and industrial collaborations, as well as by his involvement in multiple key laboratories and professional societies.
Research Interest
Dr. Liu Xiaoda’s research interests lie at the intersection of materials science and artificial intelligence. His primary focus is on materials gene engineering and machine learning applications in materials discovery. Through this integrated approach, Dr. Liu seeks to revolutionize how new materials are designed, optimized, and deployed. He is particularly interested in using data-driven algorithms to predict material properties and accelerate the development of functional materials for industrial and technological use. Another key area of his research is intelligent sensing, where he investigates the use of smart sensors and embedded systems for real-time material diagnostics. Additionally, Dr. Liu explores materials big data, employing high-throughput experimentation and data governance frameworks to manage, analyze, and visualize large-scale datasets effectively. His work contributes to both fundamental science and industrial applications, addressing pressing issues in materials innovation, corrosion control, and intelligent manufacturing. With a multidisciplinary lens, he collaborates across fields to enhance the reliability, efficiency, and environmental sustainability of next-generation materials. His future research aims to further integrate deep learning, digital twins, and autonomous experimentation to build intelligent platforms for materials development and application.
Research Skills
Dr. Liu Xiaoda possesses a comprehensive and versatile research skill set that enables him to lead innovative and impactful projects across disciplines. He is proficient in machine learning, particularly in applying algorithms such as random forests, neural networks, and support vector machines to predict and optimize material properties. His expertise in materials informatics allows him to manage and analyze large datasets for materials design using high-throughput simulations and computational modeling. He is also skilled in statistical analysis, data visualization, and programming languages such as Python, MATLAB, and R, which he uses to develop custom algorithms for scientific research. On the experimental side, Dr. Liu has hands-on experience with various characterization techniques, including XRD, SEM, and electrochemical testing, which are essential for validating computational predictions. Furthermore, he has developed skills in multidisciplinary project management, coordinating teams across research institutions and industries. His deep understanding of data governance, combined with practical skills in AI-driven decision-making, equips him to work at the forefront of intelligent materials research. His ability to bridge computational and experimental workflows is a hallmark of his research methodology, facilitating impactful outcomes in both academic and practical applications.
Awards and Honors
Dr. Liu Xiaoda has been recognized for his significant contributions to scientific research and academic leadership. He has successfully led eight major research projects, including the Shanxi International Collaboration Program, Shanxi Key R&D Plan, and foundational research initiatives. One of his achievements includes a technology transfer project based on a patented invention, demonstrating his ability to translate academic research into real-world applications. He has published over 30 scholarly articles, including 20+ SCI-indexed papers, of which four are in top-tier journals, affirming the high impact of his work. Dr. Liu holds one authorized invention patent, and his innovations have been instrumental in advancing the fields of materials data science and intelligent design. He is an active member of the China Computer Federation (CCF) and a core member of two Shanxi Provincial Key Laboratories, further emphasizing his influence in the scientific community. His growing reputation as a thought leader and his success in competitive research funding reflect his dedication to academic excellence and innovation. These accolades highlight both his intellectual depth and his practical influence on interdisciplinary research in materials and artificial intelligence.
Conclusion
Dr. Liu Xiaoda exemplifies the qualities of a modern interdisciplinary scientist, seamlessly integrating materials science with artificial intelligence to solve complex technological challenges. As an Associate Professor and Master’s Supervisor at the School of Artificial Intelligence, Taiyuan University of Technology, he has demonstrated leadership in education, research, and scientific collaboration. His commitment to pushing the boundaries of knowledge is evident in his prolific research output, influential patents, and successful management of high-level research projects. Dr. Liu’s work addresses key global priorities, including smart manufacturing, sustainable materials, and AI-driven innovation. Through his active involvement in academic societies and provincial laboratories, he continues to shape the future of intelligent materials research in China and beyond. His dynamic career trajectory and strong academic foundation make him a valuable asset to the scientific community. Looking ahead, Dr. Liu aims to deepen interdisciplinary collaborations, mentor the next generation of researchers, and further explore the synergy between data science and materials engineering. His professional journey is a testament to the power of combining traditional scientific rigor with modern computational intelligence.
Publications Top Notes
Title: Tailoring precipitation strengthening in Low-Alloy High-Strength Steel: The synergistic role of Ni, V, and Ti
Authors: Xiao-da Liu, Yu-shan Shi, Xi-wen Yue, Hua-yun Du, Li-feng Hou, Qian Wang, Huan Wei, Wen-feng Wang, Ying-hui Wei
Year: 2025
Title: Regulating the localized corrosion of grain boundary and galvanic corrosion by adding the electronegative element in magnesium alloy
Authors: Xiao-da Liu, Qixin Yan, Lifeng Hou, Junli Sun, Donghu Li, Jianlei Song, Qian Wang, Yinghui WeiYear: 2025
Citations: 2
Title: Grain gradient refinement and corrosion mechanisms in metals through severe plastic deformation: insights from Surface Mechanical Attrition Treatment (SMAT)
Authors: Xiao-da LiuYear: 2025
Citations: 1