Following the previous, successful International FAIR-DI Conference on a FAIR Data Infrastructure for Materials Genomics (2020) and the FAIR-DI workshop on the topic of a FAIR data infrastructure for materials science (2021),
we are now organizing the second
International FAIR-DI Conference on a FAIR Data Infrastructure for Materials Genomics (2022)
July 12 to 15, 2022 in Shanghai
Update: The conference has been changed to be a virtual event due to covid-19. All participants can attend the meeting free of charge!
Conference Topics:
- Data management and stewardship
- Experimental and computational databases
- Exascale computing
- High-throughput experiments and computations
- Machine learning
The conference aims to explore these topics, bring the community closer together, and foster discerning discussions and new collaborations.
Conference Chairs:
- Prof. Matthias Scheffler, Fritz Haber Institute of the Max Planck Society
- Prof. Tong-Yi Zhang, Shanghai University
Program Committee:
- Prof. Claudia Draxl, Humboldt-Universität zu Berlin
- Prof. Yi Liu, Shanghai University
- Prof. Gian-Marco Rignanese, Université catholique de Louvain
- Prof. Xiaogang Lu, Shanghai University
- Prof. Wencong Lu, Shanghai University
- Prof. Jiong Yang, Shanghai University
- Prof. Lingyan Feng, Shanghai University
Organizing Committee:
- Prof. Jincang Zhang, Shanghai University
- Prof. Quan Qian, Shanghai University
- Dr. Runhai Ouyang, Shanghai University
- Dr. Carsten Baldauf, Fritz Haber Institute of the Max Planck Society
The event is organized by the association FAIR-DI e.V., the NFDI Consortium FAIRmat, the NOMAD CoE, and the Materials Genome Institute (MGI) of Shanghai University.
Peer-reviewed Publications:
In collaboration with the FAIR-DI conference, the open-access Journal of Materials Informatics (JMI) welcomes submission of your papers to the special issue that covers the topics of data-driven materials design via high-throughput computations, high-throughput experiments, materials database, and artificial intelligence or machine learning (https://jmijournal.com/journal/special_detail/1157). JMI offers waivers for the article publishing charge for this special issue.
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