Hong Wang, Professor, School of Materials Science and Engineering, Shanghai Jiao Tong University
601 Pao Yue-Kong Library
Materials innovation has always been at the core of disruptive technical revolution. However, the conventional approach for materials development based on scientific intuition and trial-and-error effort cannot keep up with today’s ever faster demands for new materials. To meet this global challenge, Materials Genome Initiative (MGI) is launched in US, and other countries including China, with a clear goal to discover, develop, manufacture, and deploy advanced materials at twice the speed and for a fraction of the cost as of today. At the heart of MGI is a new Materials Innovation Infrastructure, featuring seamlessly integrated artificial intelligence with unprecedented large quantity of data generated by high-throughput computation and experiment. This represents a paradigm shift from “experience based experiment” to “theory based prediction and experimental verification”, which paves the way for eventually achieving the ultimate goal of “material design on need basis”.
Dr. Hong Wang joined the faculty of Shanghai Jiao Tong University in 2016 as a “Zhiyuan” Chair Professor and Director, Materials Genome Initiative Center. After earning a Ph.D. in Materials Science and Engineering from the University of Illinois at Urbana-Champaign, he worked in US as an industrial research scientist for 16 years in semiconductors, flat panel displays and large area coatings fields for global companies such as SONY, Matsushita and Guardian Industries Corp. In 2010, he became the Chief Scientist for the National Research Center for Glass Processing and Associate Director of State Key Laboratory of Green Building Materials, China Building Materials Academy in Beijing, with a research focus on the development of coated glass for energy efficient buildings, solar heat conversion materials, and smart windows. Since 2012, he’s been actively involved in promoting the Material Genome Initiative in China. His current interests are on the rapid data generation from synchrotron based high-throughput experiments and data processing using machine learning and data mining techniques.