| Abstract Scope |
Organic molecules, known as dyes, which can absorb and emit light, are potential candidates for quantum information application due to their unique excitonic properties. Importantly, exciton delocalization and coherence can occur at ambient temperature, which is crucial for building the quantum gates employed in quantum computing. To implement such an application, dye candidates are required to have high extinction coefficients, high transition dipole moments, good aggregation ability, and high exciton exchange energies. In addition, DNA is used to assemble and organize individual dyes into aggregates. To select potential candidates quickly and effectively from a large number of dyes to meet the application requirements, we have developed an integrated computational workflow, combining machine learning, density functional theory, and molecule dynamics. The computational results can inform and guide our dye synthesis and characterization experiments. |