Credit: FLEET
Just as James Cameron's Terminator-800 was able to discriminate between "clothes, boots, and a motorcycle", machine-learning could identify different areas of interest on 2D materials.
The simple, automated optical identification of fundamentally different physical areas on these materials (eg, areas displaying doping, strain, and electronic disorder) could significantly accelerate the science of atomically-thin materials.
Atomically-thin (or 2D) layers of matter are a new, emerging class of materials that will serve as the basis for next-generation energy-efficient computing, optoelectronics and future smart-phones.
"Without any supervision, machine-learning algorithms were able to discriminate between differently perturbed areas on a 2D semiconducting material," explains lead author Dr Pavel Kolesnichenko. "This can lead to fast, machine-aided characterization of 2D materials in the future, accelerating application of these materials in next-generation low-energy smart-phones."