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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium: Machine Learning and Autonomous Researchers for Materials Discovery and Design
Presentation Title Combining Simulation and Autonomous Experimentation for Mechanical Design
Author(s) Aldair Gongora
On-Site Speaker (Planned) Aldair Gongora
Abstract Scope Additive manufacturing (AM) has increased the complexity with which structures can be designed and fabricated. Computational tools, empowered by the control afforded by AM, have enabled the discovery and realization of structures with enhanced or tailored mechanical performance. However, this approach is limited to mechanical properties that can be reliably predicted using simulation. For properties that cannot be reliably simulated, such as toughness, autonomous experimental research platforms have emerged to explore the design space for high performing structures by combining automated experimentation and active learning. An open question that remains is how to effectively combine simulation, with varying degrees of accuracy and cost, and autonomous experimentation in order to accelerate learning. In this work, we evaluate a series of methods for combining simulation and autonomous experimentation.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Adaptive Machine Learning for Efficient Navigation of Materials Space
Application of Machine Learning and Federated Big Data Storage & Analytics for Accelerated Additive Process and Parameter Development
Autonomous Research Systems for Materials Development
Autonomous Systems for Alloy Design: Towards Robust Closed-loop Alloy Deposition and Characterization
Bayesian Methods for Concrete Creep Prediction and Learning Optimized Concrete Microstructure Design
Closing the Loop in Autonomous Materials Development
Combining Simulation and Autonomous Experimentation for Mechanical Design
Design of Halide Perovskites via Physics-informed Machine-learning
Turning Statistical Mechanics Models into Materials Design Engines
Unraveling Hierarchical Materials using Autonomous Research Systems

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