NSF Award
2410667
Quantum computers can perform calculations much faster and with less energy than classical computers. However, they generally require extremely low operating temperature, noise, and humidity conditions and large device footprints. Conjugated organic molecules, such as dyes, that absorb and emit light are potential candidates for room-temperature quantum computing due to their unique optical properties. The design of such dyes requires efficient high-throughput screening. A new autonomous molecular design cyberinfrastructure (CI) is developed for forward and inverse de novo materials designs. Forward design discovers the most predictive features for a target property or performance called the latent space. Inverse design predicts an optimal structure or compositions leveraging the latent space, given the desired properties and / or performance. The technologies developed in this project are integrated into a STEM education effort through course module development, and participation in the Democratizing Data Science for Climate Resiliency and Social Mobility (ClimB) project - a modular, fully online, automated, and personalized data science certificate program – and a series of K-12 outreach activities. The project aligns well with NSF’s 10 Big Ideas.<br/><br/>The project goal is to develop a visual artificial intelligence (AI)- and knowledge-driven machine learning (ML) toolkit (MatFlow) for multi-source and multi-formatted data analysis capable of both forward and inverse designs. Research objectives are to: (1) conduct ab-initio density functional theory (DFT) and time-dependent (TD) DFT to generate dye structure-property datasets; (2) create a visual CI toolkit using a user-friendly graphical interface to conduct both forward and inverse dye designs; (3) validate and test the CI toolkit by primary end users; and (4) expand end-user communities beyond quantum computing. The developed CI accelerates and advances the exploration of new molecular science and broadens the interface of organic chemistry with quantum physics, materials science, biochemistry, biophysics, computer science, and data science.<br/><br/>This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry and the Directorate for STEM Education.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
