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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Expanding the Boundaries of Materials Science: Unconventional Collaborations
Presentation Title Machine Learning for Materials Design and Discovery
Author(s) Bryce Meredig
On-Site Speaker (Planned) Bryce Meredig
Abstract Scope The rise of machine learning (ML) in materials science has led to interdisciplinary research drawing from the materials, computer science, and statistics fields. In this talk, we will describe ways in which materials design is a unique application area for ML, and also ways in which research threads from computer science and statistics are enriching materials informatics.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Materials Design Through Community, Open Data and Collaboration
Additive Manufacturing for Novel Thermal Devices
Convergence: Supporting Multidisciplinary Research at the National Science Foundation
Creating the Next-Generation Materials Genome Initiative Workforce
Innovation in Materials Research Collaborations: DOE Basic Energy Sciences
Integrating Experiment, Data, and Computations to Accelerate the Design of Materials
Machine Learning for Materials Design and Discovery
Mechanical Properties of Molecular Crystals--Connecting with Chemistry
Regularization of Materials Failure Data for Damage Mechanism Categorization by Machine Learning

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