FAIR-DI

Conference on A FAIR Data Infrastructure For Materials Genomics 2022

 

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.