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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
Sponsorship
Organizer(s) Yu-chen Karen Chen-Wiegart, Stony Brook University / Brookhaven National Laboratory
Scope There have been increasing efforts in exploring data-driven decision making and machine-learning augmented approaches for efficient materials design. An exciting recent progress is integrating these data-driven and machine-learning approaches with advanced characterization tools, such as advanced electron microscopy and synchrotron analysis, to access and to predict critical processing-structure-property relationships. The design of nano-architectured materials exhibits a complex parameter space, and thus the community could profit from applying and further developing these novel methodologies to avoid the ‘trial-and-error’ approach and to complement computational simulations and theoretical analyses.

This special symposium session thus aims to gather experts on the critical emerging topic of data-driven, machine-learning-based nano-architectured materials design. It will also place a key emphasis on novel characterization, such as autonomous synchrotron X-ray characterization and data-driven next generation transmission electron microscopy. The event will be a special session in conjunction with the regular symposium, “Self-organizing Nano-architectured Materials”. The session is intended to be interdisciplinary, covering a range of materials, such as metallic nanomaterials, self-assembling block copolymers, combinatorial thin films and electronic, magnetic and smart materials. This session will provide a forum for seemingly disparate fields to interact and drive materials discovery with novel characterization methods that are augmented by data science and machine learning.

Abstracts Due 07/19/2021
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

Machine Learning based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-Matrix-nano-composites
Accelerated Discovery of Multi-Phase Refractory Alloys Through Machine Learning Surrogate Models of CALPHAD
Autonomous X-ray Scattering for the study of Non-equilibrium Self-assembly
Designing nano-architectured materials with a machine-learning augmented framework
Discovery of nanocomposite phase change memory materials via closed-loop autonomous combinatorial experimentation
Intelligent Design of Additively Manufactured Architected Materials
Volumetric nanoscale imaging of DNA-assembled nanoparticle superlattices
“Big Data” Characterization of Material Properties and High Temperature Kinetics


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