Track G
Big Data, Artificial Intelligence and Machine Learning Methods for Accelerated Materials Discovery and Advancement

Christopher M. WOLVERTON, Northwestern University, USA

Muratahan AYKOL, Google, USA
Amanda BARNARD, The Australian National University, Australia
Juan DE PABLO, University of Chicago, USA
Claudia DRAXL, Humboldt-Universität zu Berlin, Germany
Seungbum HONG, KAIST, South Korea
Surya KALIDINDI, Georgia Institute of Technology, USA
Boris KOZINSKY, Harvard University, USA
Miguel A.L. MARQUES, Martin-Luther University, Germany
Benji MARUYAMA, Air Force Research Laboratory, USA
Bryce MEREDIG, Citrine Informatics, USA
Dane MORGAN, University of Wisconsin-Madison, USA
Tim MUELLER, John Hopkins University, Baltimore, USA
Shyue Ping ONG, University of California, San Diego, USA
Rampi RAMPRASAD, Georgia Institute of Technology, USA
Thomas SCHREFL, Donau University, Austria
Aloysius SOON, Yonsei University, South Korea
Taylor D. SPARKS, The University of Utah, USA
Aron WALSH, Imperial College London, UK
Edgar D. ZANOTTO, Federal University of Sao Carlos, Brazil
Stefano ZAPPERI, University of Milan, Italy
Xiaoying ZHUANG, Leibniz University Hannover, Germany
Milad ABOLHASANI, North Carolina State University, USA
Joshua C. AGAR, Drexel University, USA
Pieremanuele CANEPA, University of Houston, USA
Gerbrand CEDER, University of California, Berkeley, USA
GuanHua CHEN, The University of Hong Kong, Hong Kong
Wei CHEN, University of Buffalo, USA
Ekin Dogus CUBUK, Google, USA
Claudia DRAXL, Humboldt University of Berlin, Germany
Seungwu HAN, Seoul National University, South Korea
Seungbum HONG, KAIST, South Korea
Boris KOZINSKY, Harvard University, USA
Joseph MONTOYA, Toyota Research Institute, USA
Kristin PERSSON, University of California, Berkeley, USA
Gian-Marco RIGNANESE, University of Louvain, Belgium
Seunghwa RYU, KAIST, South Korea
James SAAL / Marco MUSTO, Citrine Informatics, USA
Jonathan SCHMIDT, ETH Zurich, Switzerland
Tetsuya SHOJI, Toyota Motor, Japan
Shijing SUN, University of Washington, USA
Ichiro TAKEUCHI, University of Maryland, USA
Aron WALSH, Imperial College London, UK
Yanliang ZHANG, University of Notre Dame, USA
As we approach the new era of explosive generation of big data and creative concepts of artificial intelligence and machine learning, this Track would address virtual materials design, integration of information technology and the next generation manufacturing by identifying key challenges and opportunities for big data enhanced technologies in accelerating materials innovation to face with the needs of sustainable development and industry.
Some of key topics which will be covered are high throughput materials design and characterization, big data, materials genome and informatics, machine learning, artificial intelligence aided smart manufacturing, and other information enhanced emerging technologies.
Session Topics

G-1 Advances in machine learning principles, algorithms, descriptors and databases, machine learning approaches, their interpretability and potential pitfalls

G-2 Virtual materials design and evaluation

G-3 Integrating machine learning and simulations for materials design and manufacturing

G-4 High throughput materials characterization and testing

G-5 Big data, machine learning and artificial intelligence moving towards next generation smart manufacturing and sustainable development


Cimtec 2024

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