Abstract:
This symposium aims to explore the role of machine learning (ML) in materials science, including concept development, experimental design, material synthesis, and characterization. The symposium covers a range of cutting-edge topics including the utilization of computer vision techniques for advanced data analysis and modeling, automated and autonomous synthesis and characterization, the integration of natural language processing in materials research, and strategies for effective human-AI collaboration. The goal of the symposium is to foster a collaborative environment that catalyzes the development of novel materials and advance fundamental knowledge by leveraging not only the synergy between ML algorithms and tool automation but also enhancing human-AI cooperation.
Lead Organizers:
Yongtao Liu, Oak Ridge National Laboratory, liuy3@ornl.gov
Arpan Biswas; University of Tennessee Knoxville; abiswas5@utk.edu
Yan Zeng; Florida State University; yzeng2@fsu.edu
Esther Tsai; Brookhaven National Laboratory; etsai@bnl.gov
Proposed Sessions/Topics:
-Autonomous Synthesis, Characterization, and Modeling
-Machine Learning Algorithms for Diffraction, Spectroscopy, and Imaging Analysis
-Natural Language Processing for Knowledge Extraction, Experiment Planning, and Workflow Design
-Theory in the Loop Automated Experimentation
-Physics-AI and Human-AI Synergies
-Autonomous research data management
Invited Speakers:
1. Bin Ouyang, Florida State University
2. Maria Chan, Argonne National Laboratory
3. Sumner Harris, Oak Ridge National Laboratory
4. Yishu Wang, University of Tennessee Knoxville
5. Suji Park, Brookhaven National Laboratory
6. Zhichu Ren, Massachusetts Institute of Technology
7. Rob Moore, Oak Ridge National Laboratory
Sponsored By
