| Abstract Scope |
With the emergence of efficient algorithms and advancement in electron microscopes, there is a scope to utilize theoretical models to guide, perform experiments while refining the parameters in both spaces, to establish a continuous feedback-loop. Instrument specificity, implementation complexity, information transferability by addressing fundamentally different latencies of imaging and simulations, remain the primary challenges. This presentation will focus on how deep learning (DL) frameworks are employed to extract atomic level information from 2D systems such as graphene followed by first-principles based studies to develop comprehensive understanding of the materials physics. These enable seamless deployment of several DL algorithms on-the-fly for appropriate feature finding, property predictions in combination with atomistic simulations to explore underpinning causal mechanisms, suited for guiding next set of experiments. |