  In this work we train a neural network to identify impurities in the
experimental images obtained by the scanning tunneling microscope measurements.
The neural network is first trained with large number of simulated data and
then the trained neural network is applied to identify a set of experimental
images taken at different voltages. We use the convolutional neural network to
extract features from the images and also implement the attention mechanism to
capture the correlations between images taken at different voltages. We note
that the simulated data can capture the universal Friedel oscillation but
cannot properly describe the non-universal physics short-range physics nearby
an impurity, as well as noises in the experimental data. And we emphasize that
the key of this approach is to properly deal these differences between
simulated data and experimental data. Here we show that even by including
uncorrelated white noises in the simulated data, the performance of neural
network on experimental data can be significantly improved. To prevent the
neural network from learning unphysical short-range physics, we also develop
another method to evaluate the confidence of the neural network prediction on
experimental data and to add this confidence measure into the loss function. We
show that adding such an extra loss function can also improve the performance
on experimental data. Our research can inspire future similar applications of
machine learning on experimental data analysis.
