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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Presentation Title Enabling Data-driven Discovery of Chemistry-function Relationships via Automated Packing Motif Labeling
Author(s) Donald Loveland, Phan Nguyen, Anna Hiszpanski, T. Yong-Jin Han
On-Site Speaker (Planned) Donald Loveland
Abstract Scope Models that predict bulk properties of molecular materials from chemical structures alone and apriori to synthesis are desired to accelerate materials development. While chemistry-based machine learning has advanced these efforts, insufficient data remains a challenge. The packing motif, a visually distinctive pattern in how aromatic molecules are oriented relative to one another in a crystal structure, is an example characteristic that influences many bulk properties but lacks large datasets for machine learning. Efforts to create such datasets using automated labeling tools by geometric descriptors have been stymied due to the difficulty of selecting the appropriate crystal orientation for the packing motif to be evident. We developed a procedure to identify the appropriate crystallographic plane for analysis, improving packing motif label accuracy by up to 25% compared to previous methods. With the ability to construct large motif datasets, we investigate intermolecular interactions that correlate with specific motifs helping guide synthesis efforts.


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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