Call for Papers

Please contact the symposium organizers with questions.

Computational materials science and the use of artificial intelligence (AI) and machine learning (ML) methods to mine existing databases have significantly accelerated materials design and discovery in the past few years. In addition, high throughput experiments which involve the use of automated systems and technologies to perform laboratory experiments and data collection without direct human intervention have increased the speed, reproducibility, and cost efficiency for materials synthesis and characterization. With these advances, an emerging challenge concerns the close integration of AI-assisted computation and automated experimentation in a closed loop manner, and how this data might be analyzed and interpreted for subsequent experiment planning. This symposium will provide a platform to discuss the specific opportunities and challenges in building autonomous laboratories and their underlying computational frameworks to make fully autonomous materials discovery a reality. Topics will include the development of novel AI/ML methods, experimental automation for both hardware and software, and high-throughput experimental and computational frameworks. Materials for energy-related applications such as batteries, catalysts, optoelectronics, solar cells, and fuel cells will be highlighted. This symposium intends to inform the broad materials community of the current status and future directions of developing the very promising AI-driven autonomous laboratory.

Topics will include:

• Integration of simulations with experiments in a closed loop using AI/ML
• AI-accelerated simulations of materials under operating conditions
• AI/ML driven advanced characterization of materials
• Development of digital twins for experiments
• Advanced AI/ML techniques for materials development and manufacturing
• Autonomous systems for experimental synthesis and characterization with humans out of the loop

Invited Speakers (tentative):

• Milad Abolhasani (North Carolina State University, USA)
• Gerbrand Ceder (University of California, Berkeley, USA)
• Maria Chan (Argonne National Laboratory, USA)
• Ekin Dogus Cubuk (Google DeepMind, USA)
• John Gregoire (California Institute of Technology, USA)
• Jason Hattrick-Simpers (University of Toronto, Canada)
• Jens Hauch (Forschungszentrum Jülich GmbH, Germany)
• Christoph Kreisbeck (Aixelo, USA)
• Heather Kulik (Massachusetts Institute of Technology, USA)
• Aaron Gilad Kusne (National Institute of Standards and Technology, USA)
• Marina Leite (University of California, Davis, USA)
• Eric McCalla (McGill University, Canada)
• Kristin Persson (Lawrence Berkeley National Laboratory, USA)
• Lilo Pozzo (University of Washington, USA)
• James Rondinelli (Northwestern University, USA)
• Dong-Hwa Seo (Korea Advanced Institute of Science and Technology, Republic of Korea)
• Wei Wang (Pacific Northwest National Laboratory, USA)
• Yan (Eric) Wang (Samsung Semiconductor US, USA)
• Olga Wodo (University at Buffalo, The State University of New York, USA)

Symposium Organizers