期刊
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
卷 10, 期 5, 页码 1115-1119出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.8b03797
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类别
资金
- National Institutes of Biomedical Imaging and Bioengineering [EB002804, EB001960, EB002026]
- Chemical Sciences, Geosciences and Biosciences Division of the Office of Basic Energy Sciences at the U.S. Department of Energy [DE-FG02-08ER15960]
- NIH F32 Fellowship [GM 123596]
- NSF Graduate Research Fellowship [112237]
- Martin Family Society of Fellows for Sustainability
- U.S. Department of Energy (DOE) [DE-FG02-08ER15960] Funding Source: U.S. Department of Energy (DOE)
A machine learning approach is presented for analyzing complex two-dimensional hyperfine sublevel correlation electron paramagnetic resonance (HYSCORE EPR) spectra with the proficiency of an expert spectroscopist. The computer vision algorithm requires no training on experimental data; rather, all of the spin physics required to interpret the spectra are learned from simulations alone. This approach is therefore applicable even when insufficient experimental data exist to train the algorithm. The neural network is demonstrated to be capable of utilizing the full information content of two-dimensional N-14 HYSCORE spectra to predict the magnetic coupling parameters and their underlying probability distributions that were previously inaccessible. The predicted hypes-fine (a, T) and N-14 quadrupole (K, eta) coupling constants deviate from the previous manual analyses of the experimental spectra on average by 0.11 MHz, 0.09 MHz, 0.19 MHz, and 0.09, respectively.
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