Typically, materials research involves repeated cycles of conception, design, synthesis, and characterization, which rely on human scientists for data analysis, decision-making, and manual operation of scientific tools, resulting in long timelines for new materials development. An emerging topic is combining machine learning, tool automation, and human knowledge/physical hypotheses to realize autonomous experiments, where machine learning models are used to analyze data, refine theories/hypotheses, and make decisions for future experiments. As such, an iterative loop of experiments can be carried out without human intervention and accelerate the discovery of new materials and physics.

This symposium covers topics related to the applications of machine learning in materials research, including autonomous experiments, high-throughput synthesis and characterization, and algorithms developments for materials science. This symposium aims to bring together researchers across the materials science and machine learning communities to foster and accelerate the development of novel materials and fundamental knowledge.

Proposed sessions

  • Data-driven discovery of materials and physics
  • Autonomous research
  • High-throughput experiments
  • Computer-assisted experiment automation
  • Data-driven modeling and analysis
  • Embed physics in machine learning
  • Knowledge extraction and model interpretation
  • Experiment planning and workflow design

Organizers

Yongtao Liu, Oak Ridge National Laboratory, USA, liuy3@ornl.gov

Mahshid Ahmadi; Affiliation: University of Tennessee, USA

Jason Hattrick-Simpers; Affiliation: University of Toronto, Canada

Jian Lin; Affiliation: University of Missouri, USA

Invited Speakers

Kristofer-Roy Reyes, University at Buffalo, Buffalo, NY, USA; kreyes3@buffalo.edu

Rama K. Vasudevan, Oak Ridge National Laboratory, Oak Ridge, TN, USA; vasudevanrk@ornl.gov

Ichiro Takeuchi, University of Maryland, College Park, MD, USA; takeuchi@umd.edu

Andriy Zakutayev, National Renewable Energy Laboratory, CO, USA; Andriy.Zakutayev@nrel.gov

Milad Abolhasani, North Carolina State University, Raleigh, NC, USA; mabolha@ncsu.edu

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