Symposium GI01-Advancing Materials Discovery with Data-Driven Science

Artificial intelligence offers a new and broad range of tools for materials design that require a new suite of experimental and simulation tools to be successfully employed. In particular, data must be made available in larger quantities than what is produced by conventional experiments, and communicated in forms accessible to computer software as well as humans. Additionally, the increased rates at which machine learning algorithms can adapt to new data and generate improved predictions also necessitate techniques that can evaluate material properties just as quickly. In this symposium, we propose to highlight research that demonstrates the full process of machine-learning-driven materials design: from data acquisition through model development to materials design. Of particular interest will be studies that demonstrate unique approaches for leveraging the unique capabilities of artificial intelligence, which illustrate the possible transformative role of AI will play in materials research.

• Advances in materials theory relevant for data-driven discovery
  

• New machine-learning methods for materials science

• Data parsing, digitization, structuring, storing and dissemination in materials

• Integrating materials data from multiple sources

• High-throughput synthesis and in-situ characterization

• Machine-learning, data-mining and materials-screening for materials discovery
  

• Automation of experiments and computations, including advancements in software and robotics

• Francesca Tavazza (National Institute of Standards and Technology, USA )
• John Gregoire (California Institute of Technology, USA)
• Bryce Meredig (Citrine Informatics, USA)
• Curtis Berlingette (The University of British Columbia, Canada)
• Elizabeth Holm (Carnegie Mellon University, USA)
• Ichiro Takeuchi (University of Maryland, USA)
• Santosh Suram (Toyota Research Institute, USA)
• Joshua Schrier (Haverford University, USA)
• Chad Mirkin (Northwestern University, USA)
• Sergei Kalinin (Oak Ridge National Laboratory, USA)
• Anubhav Jain (Lawrence Berkeley National Laboratory, USA)
• Jacqueline Cole (University of Cambridge, UK)
• William Chueh (Stanford University, USA)