Machine Learning Identification of Impurities in the STM Images

TL;DR

This paper presents a neural network approach trained on simulated STM images to accurately identify impurities in experimental images, addressing challenges like noise and non-universal physics.

Contribution

The study introduces a method combining simulated data with noise modeling and confidence evaluation to improve impurity identification in STM images.

Findings

Neural network trained on simulated data can identify impurities in experimental images.

Adding noise to simulated data enhances real-world performance.

Confidence-based loss function improves prediction accuracy.

Abstract

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.