Electrostatic Discovery Atomic Force Microscopy
Niko Oinonen1, Chen Xu1, Benjamin Alldritt1
1Department of Applied Physics, Aalto University, 00076 Aalto, Helsinki, Finland.
ACS Nano
|November 22, 2021
Summary
Electrostatic discovery atomic force microscopy uses machine learning to map electrostatic potential at the atomic scale. This new method provides reliable electrostatic maps for diverse molecular systems with low computational cost.
Area of Science:
- Surface Science
- Materials Science
- Computational Chemistry
Background:
- Scanning probe microscopy (SPM) offers high-resolution atomic and electronic structure imaging.
- However, reliable electrostatic characterization at the same scale remains a significant challenge for SPM techniques.
Purpose of the Study:
- To develop a machine learning-based method for atomic-scale electrostatic characterization using SPM.
- To provide immediate and reliable electrostatic potential maps directly from SPM images.
Main Methods:
- Electrostatic discovery atomic force microscopy (ED-AFM), a novel machine learning approach.
- Utilizes functionalized tips in atomic force microscopy (AFM) to acquire data.
- Generates electrostatic potential maps directly from AFM images.
Main Results:
- Successfully applied ED-AFM to characterize electrostatic properties of various molecular systems.
- Demonstrated good agreement between ED-AFM results and reference simulations.
- Achieved reliable atomic-scale electrostatic mapping with minimal computational overhead.
Conclusions:
- ED-AFM offers a powerful new tool for atomic-scale electrostatic characterization.
- The method is applicable to a wide range of molecular systems.
- Provides a computationally efficient alternative for electrostatic mapping.
Keywords:
atomic force microscopychemical identificationelectrostaticsmachine learningtip functionalization
