Atomic Force Microscopy (AFM) lies centrally within developments in nanotechnology without material restrictions and is increasingly being used for nanoscale characterisation in a wide variety of physical, biological and chemical processes. However, at the atomic scale, once the technique moves into the real world of 3D molecular structures, the link between image and interpretation becomes much more complex, and cannot be elucidated from current modelling approaches. This is a significant barrier to the wider adoption of AFM in molecular characterisation, and prevents its obvious potential being fully realised.
The CATAFM project targets an opportunity to develop a systematic machine learning software approach to understand and predict AFM images for molecules of any size, configuration or orientation. This will open the door to apply this powerful technique to a huge variety of systems where routine atomic and chemical structural resolution can be a major breakthrough.