Electrostatic Discovery Atomic Force Microscopy

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.

References in corpus (11)

• Attention U-Net: Learning Where to Look for the Pancreas
• Machine learning and the physical sciences
• Advances in atomic force microscopy
• Data-driven materials science: status, challenges and perspectives
• The mechanism of high-resolution STM/AFM imaging with functionalized tips
• Automated Structure Discovery in Atomic Force Microscopy
• Molecular structure elucidation with charge-state control
• Scanning Quantum Dot Microscopy
• Autonomous robotic nanofabrication with reinforcement learning
• Investigating Atomic Contrast in Atomic Force Microscopy and Kelvin Probe Force Microscopy on Ionic Systems using Functionalized Tips
• Single- and many-particle description of scanning tunneling spectroscopy