Symposium CB
Big Data and Machine Learning Methods for Materials Advancements
Conveners:
Fadi ABDELJAWAD, Clemson University, USA
Veena TIKARE, Sandia National Laboratories, USA
Jingyang WANG, Institute of Metal Research, CAS, China
Members:
Wai-Yim CHING, University of Missouri, USA
Jui-Sheng CHOU, Taiwan Tech, Taiwan
Paolo COLOMBO, Università di Padova, Italy
Masahiko DEMURA, NIMS, Japan
Raynald GAUVIN, McGill University, Canada
Robert J. HANISCH, NIST, USA
Satoshi HATA, Kyushu University, Japan
Elizabeth HOLM, Carnegie Mellon University, USA
Surya KALIDINDI, Georgia Institute of Technology, USA
Sergei V. KALININ, ORNL, USA
Kwang-Ryeol LEE, Korea Institute of Science and Technology, Korea
Hua-Tay LIN, Guangdong University of Technology, China
Bryce MEREDIG, Citrine Informatics, USA
Alexander MICHAELIS, Fraunhofer Institute for Ceramic Technologies and Systems, Germany
Tatsuki OHJI, AIST, Japan
Ivar REIMANIS, Colorado School of Mines, USA
Sudipta SEAL, University of Central Florida, USA
Dileep SINGH, Argonne National Laboratory, USA
Veera SUNDARARAGHAVAN, University of Michigan, Ann Arbor, USA
Ichiro TAKEUCHI, University of Maryland, USA
Isao TANAKA, Kyoto University, Japan
Aron WALSH, Imperial College London, UK
Francois WILLOT, MINES ParisTech, France
Chris, WOLVERTON, Northwestern University, USA
Winnie WONG-NG, NIST, USA
Fadi ABDELJAWAD, Clemson University, USA
Veena TIKARE, Sandia National Laboratories, USA
Jingyang WANG, Institute of Metal Research, CAS, China
Members:
Wai-Yim CHING, University of Missouri, USA
Jui-Sheng CHOU, Taiwan Tech, Taiwan
Paolo COLOMBO, Università di Padova, Italy
Masahiko DEMURA, NIMS, Japan
Raynald GAUVIN, McGill University, Canada
Robert J. HANISCH, NIST, USA
Satoshi HATA, Kyushu University, Japan
Elizabeth HOLM, Carnegie Mellon University, USA
Surya KALIDINDI, Georgia Institute of Technology, USA
Sergei V. KALININ, ORNL, USA
Kwang-Ryeol LEE, Korea Institute of Science and Technology, Korea
Hua-Tay LIN, Guangdong University of Technology, China
Bryce MEREDIG, Citrine Informatics, USA
Alexander MICHAELIS, Fraunhofer Institute for Ceramic Technologies and Systems, Germany
Tatsuki OHJI, AIST, Japan
Ivar REIMANIS, Colorado School of Mines, USA
Sudipta SEAL, University of Central Florida, USA
Dileep SINGH, Argonne National Laboratory, USA
Veera SUNDARARAGHAVAN, University of Michigan, Ann Arbor, USA
Ichiro TAKEUCHI, University of Maryland, USA
Isao TANAKA, Kyoto University, Japan
Aron WALSH, Imperial College London, UK
Francois WILLOT, MINES ParisTech, France
Chris, WOLVERTON, Northwestern University, USA
Winnie WONG-NG, NIST, USA
Fadi ABDELJAWAD, Clemson University, USA (Keynote)
Boris KOZINSKY, Harvard University, USA
Jian LUO, University of California, San Diego, USA
Terayasu MIZOGUCHI, The University of Tokyo, Japan
Giovanni PIZZI / Marnik BERCX, EPFL, Switzerland
Thomas SCHREFL, Danube University Krems, Austria
Ichiro TAKEUCHI, University of Maryland, USA
Gerard L. VIGNOLES, University of Bordeaux, France (Keynote)
Chris WOLVERTON, Northwestern University, USA (Keynote)
Boris KOZINSKY, Harvard University, USA
Jian LUO, University of California, San Diego, USA
Terayasu MIZOGUCHI, The University of Tokyo, Japan
Giovanni PIZZI / Marnik BERCX, EPFL, Switzerland
Thomas SCHREFL, Danube University Krems, Austria
Ichiro TAKEUCHI, University of Maryland, USA
Gerard L. VIGNOLES, University of Bordeaux, France (Keynote)
Chris WOLVERTON, Northwestern University, USA (Keynote)
The traditional way of innovations and development in materials field are human-centred, where scientists and engineers design, conduct, analyse and interpret results obtained through simulations, experiments, or literature review. Such results are often high-dimensional, huge in number and heterogeneous in nature, which hinders researcher’s capability to draw insight from extensive information. As we approach the new era of explosive generation of big data and creative concept of artificial intelligence and machine learning, we may envisage a completely different paradigm for generating knowledge and advancing technology. Machine-aided innovation will accelerate important leaps towards better and more affordable solutions for the sustainable development of human society. Big data enhanced emerging technologies would be able to pioneer the new paradigm to discover truth beyond information and generate knowledge.
This Symposium, endorsed by the World Academy of Ceramics, features two sessions. The first one would address virtual materials design, integration of information technology and the next-generation manufacturing. The technical program will identify key challenges and opportunities for big data enhanced technologies in accelerating materials innovation and creating sustainable development. Some of the key topics which will be covered are high throughput materials design and characterization, artificial intelligence aided smart manufacturing, and other information enhanced emerging technologies for sustainable advancements of ceramic materials.
Quantification of microstructure by data analysis and machine learning methods will be of specific interest for the second session. Indeed, Central to materials science are the links between processing and microstructure, and between microstructure and properties. Advances made in recent years include the ability to image microstructures over large areas with high resolution using multiple electron beams that raster simultaneously in SEM and obtaining high resolution 3D images using tomography or reconstruction methods. Developing methods to quantify size, shape, curvature, texture, distributions of these quantities, topology, connectivity, and spatial correlations between such characteristics of microstructure features would greatly enhance our ability to design and fabricate ceramics microstructures to optimize properties.
The overall symposium is expected to generate interest and discussion on how the ceramic community might facing the challenges of sustainable development and industry 4.0.
Contributions are welcome in each of the following Session Topics:
This Symposium, endorsed by the World Academy of Ceramics, features two sessions. The first one would address virtual materials design, integration of information technology and the next-generation manufacturing. The technical program will identify key challenges and opportunities for big data enhanced technologies in accelerating materials innovation and creating sustainable development. Some of the key topics which will be covered are high throughput materials design and characterization, artificial intelligence aided smart manufacturing, and other information enhanced emerging technologies for sustainable advancements of ceramic materials.
Quantification of microstructure by data analysis and machine learning methods will be of specific interest for the second session. Indeed, Central to materials science are the links between processing and microstructure, and between microstructure and properties. Advances made in recent years include the ability to image microstructures over large areas with high resolution using multiple electron beams that raster simultaneously in SEM and obtaining high resolution 3D images using tomography or reconstruction methods. Developing methods to quantify size, shape, curvature, texture, distributions of these quantities, topology, connectivity, and spatial correlations between such characteristics of microstructure features would greatly enhance our ability to design and fabricate ceramics microstructures to optimize properties.
The overall symposium is expected to generate interest and discussion on how the ceramic community might facing the challenges of sustainable development and industry 4.0.
Contributions are welcome in each of the following Session Topics:
Session Topics
CB-1 Big data emerging technologies for innovative materials design and manufacturing
- Virtual materials design and evaluation
- High-throughput characterization and testing
- Big data methodologies and integration of information technology
- Machine learning and artificial intelligence
- Smart manufacturing
- High-throughput characterization and testing
- Big data methodologies and integration of information technology
- Machine learning and artificial intelligence
- Smart manufacturing
CB-2 Quantification of materials characterization by data analysis and machine learning methods
- Automation of stereological techniques to characterize microstructure
- Data analysis for quantitative description of microstructure
- Machine learning to recognize microstructural features and quantify microstructures
- Development of long-range descriptors
- Application of data analytics or machine learning to find processing-microstructure or microstructure-property relationships
- Data analysis for quantitative description of microstructure
- Machine learning to recognize microstructural features and quantify microstructures
- Development of long-range descriptors
- Application of data analytics or machine learning to find processing-microstructure or microstructure-property relationships