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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Presentation Title Polymer Informatics—Current Status and Critical Next Steps
Author(s) Lihua Chen, Rampi Ramprasad
On-Site Speaker (Planned) Lihua Chen
Abstract Scope The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. Here, we describe the role played by computational and experimental data generation and capture, polymer fingerprinting, machine-learning based property prediction models, and algorithms for designing polymers meeting target property requirements. These efforts have culminated in the creation of an online Polymer Informatics platform (https://www.polymergenome.org) to guide ongoing and future polymer discovery and design. Challenges that remain will be examined, and systematic steps that may be taken to extend the applicability of such informatics efforts to a wide range of technological domains will be discussed. These include strategies to deal with the data bottleneck, new methods to represent polymer morphology and processing conditions, and the applicability of emerging algorithms for design.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Hybrid EBSD Indexing Method Powered by Convolutional Neural Network (CNN) and Dictionary Indexing (DI)
Directing Matter In-situ via Deep Learning
Enabling Data-driven Discovery of Chemistry-function Relationships via Automated Packing Motif Labeling
Image Characterization of Self-assembled Photonic Crystals and Glasses Using Machine Learning
Instance Segmentation for Autonomous Detection of Individual Powder Particles and Satellites in an Additive Manufacturing Feedstock Powder
Inverse Design of Porous Structures by Deep Learning and TPU-based Computing
Polymer Informatics—Current Status and Critical Next Steps
The Composition-microstructure-property Relationship by Machine Learning

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