報告人：Ming Hu,Department of Mechanical Engineering, University of South Carolina, USA.
報告時(shí)間：2020年12月10日(周四)10：00
報告地點(diǎn)：建筑館211、線(xiàn)上
報告人簡(jiǎn)介：
Ming Hu is currently a Professor in the Department of Mechanical Engineering at the University of South Carolina. He received his B.S. degree in mechanical engineering from University of Science and Technology of China (USTC) in 2001 and then his PhD in solid mechanics from the Institute of Mechanics, Chinese Academy of Sciences in 2006. He was a research associate at the Rensselaer Polytechnique Institute from 2006 to 2009 and then a senior research scientist at the Swiss Federal Institute of Technology from 2009 to 2013. In 2013 he joined the Faculty of Materials Engineering at the RWTH Aachen University in Germany as an Assistant Professor. He accepted his current position in the Department of Mechanical Engineering at the University of South Carolina in 2018. Dr. Hu has more than 19 years’ experience in computer modeling and simulation of thermal transport in advanced energy systems with applications in advanced thermal management and energy systems. Dr. Hu is currently leading the Advanced Materials Discovery and Simulation Laboratory focusing on developing big data and machine learning algorithms to discover and design novel materials and structures for advanced energy engineering and technology. Dr. Hu has authored and co-authored four book chapters and 148 high-impact international journal articles with more than 4,700 citations (Google scholar h-index: 39). Dr. Hu has been invited as keynote speakers and journal reviewer by more than 50 times. He is currently an editorial board member of Scientific Reports – an online multidisciplinary journal from the publishers of Nature.
報告內容簡(jiǎn)介：
The demand for advanced materials to enable new energy technologies has exceeded the capabilities of traditional materials and chemical processes. Accessibility to credible data on materials properties and behavior—and tools that can rapidly utilize this data—are critical to expedite the design and discovery of new materials which traditionally can take 10 to 20 years. The Materials Genome Initiative (MGI) for Global Competitiveness, launched in 2011, supports efforts to accelerate materials discovery and design. The MGI aims to provide the infrastructure and training that innovators need to discover, develop, manufacture, and deploy advanced materials at least twice as fast as possible today, at a fraction of the cost. Advances in research techniques, high performance computing, and the increasing availability of materials data is enabling significant progress in the design of innovative materials. Experts note progress in developing new materials using high-throughput experimental techniques and artificial intelligence (AI) based tools that can reduce materials development time by half. However, challenges still exist, and the materials design process remains relatively slow. Exponential improvements in information technology are creating new opportunities to modernize and expand materials research tools and data analytics to streamline design and discovery. In this seminar talk, I will summarize the recent progress in AI accelerated materials discovery that enables to generate and process massive amounts of data could allow for unprecedented changes in how data can be applied to intuitive, intelligent materials design. In addition, I will present some AI related applications of exploring new materials for energy technologies, including but not limited to clean and renewable energy materials (solar cells, thermoelectrics, batteries, etc.). These examples highlight the nature of interdisciplinary research of AI accelerated materials discovery that usually bridges the gap between computer scientists and material scientists (both experimental and computational).
建筑工程與力學(xué)學(xué)院
2020年12月8日
