Self-Driving Microscopes to Navigate the Nanoscale | IEEE Spectrum

This article refers to a new study in Nature by Yicong Wu, MBL Fellows Hari Shroff, Patrick La Rivière, and Daniel Colón-Ramos, and colleagues.

It's difficult to find an area of scientific research where deep learning isn't discussed as the next big thing. Claims abound: deep learning will spot cancers; it will unravel complex protein structures; it will reveal new exoplanets in previously-analyzed data; it will even discover a theory of everything. Knowing what's real and what's just hype isn't always easy.

One promising—perhaps even overlooked—area of research in which deep learning is likely to make its mark is microscopy. In spite of new discoveries, the underlying workflow of techniques such as scanning probe microscopy (SPM) and scanning transmission electron microscopy (STEM) has remained largely unchanged for decades. Skilled human operators must painstakingly set up, observe, and analyze samples. Deep learning has the potential to not only automate many of the tedious tasks, but also dramatically speed up the analysis time by homing in on microscopic features of interest. Read more of the article here ...

Source: Self-Driving Microscopes to Navigate the Nanoscale – IEEE Spectrum