The novel computational method created by Ramprasad’s research team applies basic quantum mechanics to calculate the atomic and electronic structures of different polymers. This information is then used to “train” a machine learning model to make ultra-fast predictions of properties of new polymers. The machine learning model recognizes polymers based on their numerical representations or “fingerprints.” With this approach, materials scientists can quickly predict the electronic properties of a new polymer, such as its band gap (the amount of energy it takes for an electron to break free of its home atom in the polymer), and its dielectric constant (a measure of the effect an electrical field has on the polymer). Those bits of information, and other relevant information that will be incorporated into the system as part of the TRI initiative, are crucial to scientists looking to create new materials that will improve electrochemical energy storage devices like fuel cells and batteries.
Ramprasad and his research team are current participants in the Spring 2017 Accelerate UConn cohort taught by CCEI.