DNA can stabilize silver clusters with sequence-tuned fluorescence. We review recent advances in understanding of the structures and properties of DNA-templated silver clusters, their rational design and ordered arrangement, and emerging challenges.

DNA oligomers are known to serve as stabilizing ligands for silver nanoclusters (Ag N -DNAs) with rod-like nanocluster geometries and nanosecond-lived fluorescence. Here, we report two Ag N -DNAs that possess distinctly different structural properties and are the first to exhibit only microsecond-lived luminescence. These emitters are characterized by significant broadband downconversion from the ultraviolet/visible to the near-infrared region. Circular dichroism spectroscopy shows that the structures of these two Ag N -DNAs differ significantly from previously reported Ag N -DNAs. We find that these nanoclusters contain eight valence electrons, making them the first reported DNA-stabilized luminescent quasi-spherical superatoms. This work demonstrates the important role that nanocluster composition and geometry play in dictating luminescence properties of Ag N -DNAs and significantly expands the space of structure–property relations that can be achieved for Ag N -DNAs.

DNA can stabilize silver nanoclusters (Ag N -DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA’s vast sequence space challenges the design and discovery of Ag N -DNAs with tailored properties. In particular, Ag N -DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive Ag N -DNAs. By combining high-throughput experimentation and machine learning with fundamental information from Ag N -DNA crystal structures, we distill the salient DNA sequence features that determine Ag N -DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of Ag N -DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive Ag N -DNAs by 12.3 times as compared to training data, nearly doubling the number of known Ag N -DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure–property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data.