Reconstructing Pristine Molecular Orbitals from Scanning Tunneling Microscopy Images via Artificial Intelligence Approaches

TL;DR

This paper introduces STM-Net, a deep learning framework that reconstructs high-resolution molecular orbitals from STM images, overcoming previous resolution limitations and enabling detailed chemical analysis.

Contribution

The authors develop a physics-driven deep learning network that accurately reconstructs molecular orbitals from STM images, accounting for tip contributions and broad experimental conditions.

Findings

STM-Net successfully reconstructs pristine molecular orbitals from experimental STM images.

The method adapts to various tip states and substrates, demonstrating broad applicability.

High-resolution MO characterization is achieved, enhancing chemical analysis capabilities.

Abstract

Molecular orbital (MO) is one of the most fundamental concepts for molecules, relating to all branches of chemistry, while scanning tunneling microscopy (STM) has been widely recognized for its potential to measure the spatial distribution of MOs. However, the precise characterization of MO with high resolution in real space is a long-standing challenge owing to the inevitable interference of high-angular-momentum contributions from functionalized tips in STM. Here, leveraging advances in artificial intelligence for image recognition, we establish a physics-driven deep-learning network, named STM-Net, to reconstruct MOs from high-resolution STM images with a functionalized tip, taking advantage of the separable characteristics of different angular momentum contributions. We demonstrate that STM-Net can be directly applied to a variety of experimental observations, successfully reconstructing pristine MO features for molecules under diverse conditions. Moreover, STM-Net can adapt to various states of the functionalized tip and the substrate, illustrating the broad applicability of our physics-driven framework. These results pave the way for accurate characterization of MO with high resolution, potentially leading to new insights and applications for this fundamental concept in chemistry.