  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.
