Sbaiti, Bashir, Schultz, Jonathan D, Parker, Kelsey A, and Beratan, David N. Machine Learning for Video Classification Enables Quantifying Intermolecular Couplings from Simulated Time-Evolved Multidimensional Spectra. Retrieved from https://par.nsf.gov/biblio/10649100. The Journal of Physical Chemistry Letters 16.19 Web. doi:10.1021/acs.jpclett.5c00588.

Sbaiti, Bashir, Schultz, Jonathan D, Parker, Kelsey A, & Beratan, David N. Machine Learning for Video Classification Enables Quantifying Intermolecular Couplings from Simulated Time-Evolved Multidimensional Spectra. The Journal of Physical Chemistry Letters, 16 (19). Retrieved from https://par.nsf.gov/biblio/10649100. https://doi.org/10.1021/acs.jpclett.5c00588

Sbaiti, Bashir, Schultz, Jonathan D, Parker, Kelsey A, and Beratan, David N. "Machine Learning for Video Classification Enables Quantifying Intermolecular Couplings from Simulated Time-Evolved Multidimensional Spectra". The Journal of Physical Chemistry Letters 16 (19). Country unknown/Code not available: ACS. https://doi.org/10.1021/acs.jpclett.5c00588. https://par.nsf.gov/biblio/10649100.

@article{osti_10649100,
place = {Country unknown/Code not available}, title = {Machine Learning for Video Classification Enables Quantifying Intermolecular Couplings from Simulated Time-Evolved Multidimensional Spectra}, url = {https://par.nsf.gov/biblio/10649100}, DOI = {10.1021/acs.jpclett.5c00588}, abstractNote = {Not Available}, journal = {The Journal of Physical Chemistry Letters}, volume = {16}, number = {19}, publisher = {ACS}, author = {Sbaiti, Bashir and Schultz, Jonathan D and Parker, Kelsey A and Beratan, David N}, }