Fig. 3
From: Learning surface molecular structures via machine vision
Generating and analyzing synthetic data. a Schematics for generating synthetic data for each molecule. Left: DFT-simulated STM images of bowl-up and bowl-down configurations. Right: Same images corrupted by admixing a proximate in energy azimuthal rotational state, blurring (effect of STM tip) and Poisson noise. b–d Application of graphical Markov model to synthetic data generated by Markov chain Monte Carlo sampler using inputs from density functional theory calculations of electronic charge density distribution in molecules. The number of molecules in synthetic dataset is 1225. b Intensity distribution for synthetic data. Two logistic functions overlaid (see text for details). c Real space distribution of molecules in synthetic dataset. d MRF based decoding of D and U states from image in c. The D and U states are denoted by red and blue, respectively. The total error (ratio of misidentified states) was 0.33 % or about 4 (out of 1225) molecules. One of the misidentified state is denoted by circle in the inset in d
