Machine learning at the (sub)atomic scale: next generation scanning probe microscopy
Oliver Gordon, Philip Moriarty
Abstract
Abstract We discuss the exciting prospects for a step change in our ability to map and modify matter at the atomic/molecular level by embedding machine learning algorithms in scanning probe microscopy (with a particular focus on scanning tunnelling microscopy, STM). This nano-AI hybrid approach has the far-reaching potential to realise a technology capable of the automated analysis, actuation, and assembly of matter with a precision down to the single chemical bond limit.
Topics & Concepts
Scanning tunneling microscopeMicroscopyScanning probe microscopyScanning capacitance microscopyNanotechnologyScanning ion-conductance microscopyAtomic unitsFocus (optics)Atomic force microscopyConductive atomic force microscopyMaterials scienceEmbeddingComputer scienceScale (ratio)Artificial intelligencePhysicsScanning confocal electron microscopyOpticsQuantum mechanicsForce Microscopy Techniques and ApplicationsElectronic and Structural Properties of OxidesAdvanced Materials Characterization Techniques