Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

Abstract

DNA can stabilize silver nanoclusters (AgN-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 AgN-DNAs with tailored properties. In particular, AgN-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 AgN-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from AgN-DNA crystal structures, we distill the salient DNA sequence features that determine AgN-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of AgN-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive AgN-DNAs by 12.3 times as compared to training data, nearly doubling the number of known AgN-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.