Machine learning for the discovery of molecular recognition based on single-walled carbon nanotube corona-phases
Name
s41524-022-00795-7.pdf
Description
Published version
Size
1.43 MB
Format
Adobe PDF
Checksum (MD5)
73329b57fea255a10fcd9df70744de7c
Author(s)
Gong, Xun
Renegar, Nicholas
Levi, Retsef
Strano, Michael S
Date Issued
December 2022
Journal
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Citation
Gong, Xun, Renegar, Nicholas, Levi, Retsef and Strano, Michael S. 2022. "Machine learning for the discovery of molecular recognition based on single-walled carbon nanotube corona-phases." npj Computational Materials, 8 (1).
Version
Final published version
Abstract
AbstractNanoparticle corona phase (CP) design offers a unique approach toward molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs can additionally transduce MR through its band-gap photoluminescence (PL). While DNA oligonucleotides have been used as SWCNT CPs, no generalized scheme exists for MR prediction de novo 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 MR and DNA sequence dependence through local (LFs) and high-level features (HLFs). 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 uniquely correlated with different analytes. 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41524-022-00795-7
