|About this Abstract
|Materials Science & Technology 2020
|AI for Big Data Problems in Imaging, Modeling and Synthesis
|Enabling Data-driven Discovery of Chemistry-function Relationships via Automated Packing Motif Labeling
|Donald Loveland, Phan Nguyen, Anna Hiszpanski, T. Yong-Jin Han
|On-Site Speaker (Planned)
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.