Electrostatic Discovery Atomic Force Microscopy

TL;DR

This paper introduces a machine learning-based method called Electrostatic Discovery Atomic Force Microscopy that enables rapid, quantitative electrostatic potential mapping at the atomic scale from AFM images, enhancing atomic resolution characterization.

Contribution

It presents a novel ML approach for direct electrostatic potential mapping from AFM images, significantly improving reliability and efficiency at the atomic scale.

Findings

Good agreement with reference simulations

Applicable to various molecular systems

Minimal computational overhead

Abstract

While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic characterization at the same scale. In this work, we introduce Electrostatic Discovery Atomic Force Microscopy, a machine learning based method which provides immediate quantitative maps of the electrostatic potential directly from Atomic Force Microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach opens the door to reliable atomic scale electrostatic maps on any system with minimal computational overhead.