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About this Abstract
Meeting	MS&T22: Materials Science & Technology
Symposium	AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title	AI-enabled Platform for Autonomous Experimentation and Materials Discovery
Author(s)	Henry Chan, Chengshi Wang, Jie Xu, Rohit Batra, Arun Baskaran, Maria Chan, Pierre T Darancet
On-Site Speaker (Planned)	Henry Chan
Abstract Scope	The discovery of new materials is at the core of many advancements in our society. Despite decades of materials research, inverse design of functional materials has remained a grand challenge, largely due to difficulties associated with the navigation of a vast search space and the mapping of complex relationships between materials structures, properties, and synthesis/processing conditions. Recently, the application of AI/ML techniques on robotics and high-throughput instruments in laboratories has led to the active development of various Materials Acceleration Platforms (MAP), aimed to revolutionize the traditional materials discovery approach. This talk highlights the development of Polybot, a MAP developed at the Center for Nanoscale Materials, and discusses its potential in addressing problems related to the reliability of experimental data, handling of heterogeneous data, and the coupling of experiments with AI/ML and simulations.

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