(656g) Machine Learning for the Discovery of Molecular Recognition Based on Single-Walled Carbon Nanotube Corona-Phases

The nanoparticle corona phase (CP) offers a unique materials design space for constructs capable of molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs have the additional ability to transduce MR through its band gap photoluminescence (PL). DNA oligonucleotides are well-known to disperse SWCNTs through forming CPs and can be manufactured with molecular precision. Nevertheless, no generalized scheme exists for the de novo prediction of SWCNT MR based on these CPs due to their sequence-dependent three-dimensional complexity. This work generated the largest DNA-SWCNT PL response library of 1408 elements and leveraged machine learning (ML) techniques to understand the DNA sequence dependence of MR. Both local features (LFs) and high-level features (HLFs) of the DNA sequences were utilized as model inputs. Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of 8 experimental conditions. Different HLF combinations were found to be correlated with sensor responses for each analyte, offering mechanistically differentiable design parameters for these systems. Furthermore, models utilizing both LFs and HLFs show improvement over that with HLFs alone, demonstrating that DNA-SWCNT CP engineering is more complex than simply specifying molecular properties. Taken as a whole, this work details the feasibility and utility of a ML-guided approach for nanoparticle CP engineering with relatively few experiments within a high-dimensional design space.