Organic electronics could allow companies to print electronics like paper or incorporate them into clothing to power wearable electronics—if there were only better ways to control their electronic structure.
To help address this challenge, Nick Jackson, a postdoctoral fellow in the University of Chicago’s Institute for Molecular Engineering, developed a faster way of creating molecular models by using machine learning. The models dramatically accelerate the screening of potential new organic materials for electronics, and could also be useful in other areas of materials science research.
Many believe organic electronics have the potential to revolutionize technology with their high cost-efficiency and versatility, but the current manufacturing processes used to produce these materials are sensitive, and the internal structures are extremely complex. This makes it difficult for scientists to predict the final structure and efficiency of the material based on manufacturing conditions.
Shortly after Jackson began his appointment under Juan de Pablo, the Liew Family Professor in Molecular Engineering at the University of Chicago, he had the idea to tackle such problems with machine learning. He uses this technique—a way of training a computer to learn a pattern without being explicitly programmed—to help make predictions about how the molecules will assemble.