Property optimization of new materials is a slow and laborious process. Techniques to vastly improve the time taken to design processing methods to target particular properties are required for next generation materials manufacturing.
Data-driven experiments and materials informatics can help solve this problem.
Leveraging process-property relationships via mining literature data
The scientific literature is rich with process-property relationships for any given material system. In this work we developed two material databases to aid hypothesis formulation for promising future experimentation. While these databases are noisy, sparse from unreported data, and small relative to the number of design variables, analysis of high performing samples can help reduce the dimensionalilty. This approach was demonstrated on P3HT for organic electronics and polypropylene-talc for high strength composites.
- "Best practices for reporting organic field effect transistor device performance", Choi, D.; Chu, P.; McBride, M.; Reichmanis, E. (Associate Editor), Chemistry of Materials, (2015). Link
- "Silicon Valley meets the ivory tower: Searchable data repositories for experimental nanomaterials research", Persson, N.; McBride, M.; Grover, M.A.; Reichmanis, E. (2016). Link
- "Solving materials' small data problem with dynamic experimental databases", McBride, M.; Persson, N.; Reichmanis, E.; Grover, M.A. (2018). Link
- "Machine learning approaches for extracting process-structure-property relationships from experimental data and literature" in Big, Deep, and Smart Data in the Physical Sciences. Under Review.
Control and Optimization of Solution State Processing for the Design of Organic Electronic Applications
Polymer based organic electronic devices is a complex system in which the observed properties depending heavily on the structure, which in turn depends on the processing. Quantification of robust processing-structure-property relationships are required to enable organic electronics manufacturing. While a wealthy of processing methods for the workhorse semiconducting polymer, P3HT, have been developed, significant gains in both electrical performance and mechanistic understanding can be achieved via data-driven experimentation.
- "Ordering of Poly(3-hexylthiophene) in Solutions and Films: Effects of Fiber Length and Grain Boundaries on Anisotropy and Mobility", Kleinhenz, N.; Persson, N.; Xue, Z.; Chu, P.; Wang, G.; Yuan, Z.; McBride, M.; Choi, D.; Grover, M.A.; Reichmanis, E. (2016).
- "Automated Analysis of Orientational Order in Images of Fibrillar Materials”, Persson, N.; McBride, M.; Grover, M.A.; Reichmanis, E.(2016).
- "Toward Precision Control of Nanofiber Orientation in Conjugated Polymer Thin Films: Impact on Charge Transport”, Chu, P.; Kleinhenz, N.; Persson, N.; McBride, M.; Hernandez, J.; Fu, B.; Zhang, G.; Reichmanis, E. (2016).
- "Nucleation, Growth, and Alignment of Poly(3-hexylthiophene) Nanofibers for High-Performance OFETS.” Persson, N.; Chu, P.; McBride, M.; Grover, M.A.; Reichmanis, E. (2017).
- “Versatile Interpenetrating Polymer Network Approach to Robust Stretchable Electronic Devices”, Zhang, G.; McBride, M.; Persson, N.; Lee, S.; Dunn, T.; Toney, M.; Yuan, Z.; Kwon, Y.; Chu, P.; Risteen, B.; Reichmanis, E. (2017).
- "High-Throughout Image Analysis of Fibrillar Materials: A Case Study on Polymer Nanofiber Packing, Alignment, and Defects in Organic Field Effect Transistors”, Persson, N.; Rafshoon, J.; Naghshpour, K.; Fast, T.; Chi, P.; McBride, M.; Risteen, B.; Grover, M.A.; Reichmanis, E. (2017).
- “Process-Structure-Property Relationships for Design of Polymer Organic Electronics Manufacturing”, McBride, M.; Morales, C.; Reichmanis, E.; Grover, M.A. (2018).
- NSF IGERT: Nanomaterials for Energy Storage and Conversion Trainee to relate energy materials development with public policy issues