Call for Papers

Please contact the symposium organizers with questions.

The conventional materials innovation cycle heavily relies on human decision-making and manual operation of scientific tools, leading to slow progress. Pressing challenges like the electrification of everything, large-scale materials synthesis, waste stream upconversion, and energy conversion and storage demand a transformative approach to accelerate material discoveries. In this symposium, we aim to explore innovative methods that combine experimental automation and machine learning to conduct materials research at or beyond the state of the art. This convergence presents a unique opportunity for machine learning-driven autonomous experimentation, promising improved efficiency, accuracy, and reproducibility in materials synthesis and characterization, thus accelerating breakthroughs in materials and physics.

The symposium's primary focus is on showcasing the applications of machine learning in experimental tasks, with an emphasis on materials synthesis and characterization. The topics to be covered include automated and autonomous experiment workflow design, development of task-specific algorithms for experimentation, high-throughput synthesis and characterization, and the creation of digital twins for laboratories. By bringing together researchers from both the material science and machine learning communities, we aim to facilitate knowledge exchange, share recent advancements, and discuss the opportunities and challenges in this rapidly evolving field.

Topics will include:

• Computer-vision based automated experiments
• Modular high-throughput experiments
• AI-driven autonomous experiments
• Multi-fidelity workflow design
• Algorithms for microscopy, spectroscopy, diffraction, and electrochemical experiments
• Data-driven experiment planning, realization, and review
• Automation beyond the benchtop, integration across the lab and countries
• Digital twins and Ontologies in academic research contexts
• Orchestration of autonomous campaigns with multiple tenants
• Autonomous research data management

Invited Speakers:

• Milad Abolhasani (North Carolina State University, USA)
• Mahshid Ahmadi (The University of Tennessee, Knoxville, USA)
• Alan Aspuru-Guzik (University of Toronto, Canada)
• Hannah-Noa Barad (Bar-Ilan University, Israel)
• Keith Brown (Boston University, USA)
• John Gregoire (California Institute of Technology, USA)
• Jason Hattrick-Simpers (University of Toronto, Canada)
• Kedar Hippalgaonkar (National University of Singapore, Singapore)
• Pinshane Huang (University of Illinois at Urbana-Champaign, USA)
• Yoosung Jung (Korea Advanced Institute of Science and Technology, Republic of Korea)
• Eun-Ah Kim (Cornell University, USA)
• Alfred Ludwig (Ruhr-Universität Bochum, Germany)
• Benji Maruyama (Air Force Research Laboratory, USA)
• Nicola Marzari (École Polytechnique Fédérale de Lausanne, Switzerland)
• Thomas Morris (Brookhaven National Laboratory, USA)
• Dan Olds (Brookhaven National Laboratory, USA)
• Kishna Rajan (University at Buffalo, The State University of New York, USA)
• Sebastian Siol (Empa–Swiss Federal Laboratories for Materials Science and Technology, Switzerland)
• Steven R. Spurgeon (Pacific Northwest National Laboratory, USA)
• Esther Tsai (Brookhaven National Laboratory, USA)
• Daniela Ushizima (Lawrence Berkeley National Laboratory, USA)
• Rama K. Vasudevan (Oak Ridge National Laboratory, USA)
• Yan Zeng (Lawrence Berkeley National Laboratory, USA)

Symposium Organizers