Eric Stach1 Benji Maruyama2

1, University of Pennsylvania, Philadelphia, Pennsylvania, United States
2, Air Force Research Laboratory (AFRL), Dayton, Ohio, United States

In this presentation, we will describe a vision for a future research paradigm, wherein a tight coupling of in-situ and operando experimental methods, data analytics and automated data analysis are coupled with artificial intelligence to direct how we use electron microscopy to characterize the mechanisms by which processing/structure/property relationships are determined. The presentation will be forward looking, and will incorporate research results and ideas culled from a variety of sources and authors: it will not be a typical presentation reviewing research. First, we will describe the motivations for working towards this type of research paradigm. These include the desire to speed up the rate of scientific discovery and time to market, as well as a more pedestrian desire to maximize the utilization of expensive instrument time. Second, we will review examples of autonomous research methods, both through data mining of the literature [1], and through the use of real time feedback and artificial intelligence methods to direct experimental outcomes.[2] Specifically, we will discuss how these approaches may be utilized in electron microscopy research in the near future, and the developments needed to bring this to reality. Third, we will describe how this approach can be used to explicitly and efficiently test operative hypotheses, and to efficiently understand the relevant experimental parameter space. This yields insight as to where detailed experimentation is most valuable. In this portion of the talk, the need for operando methods and correlative experimentation will be emphasized.[3] Finally, we will discuss this evolving research paradigm as it exists both within and provides challenges to established theories of scientific discovery.[4,5]

1. Science Mapping: A Systematic Review of the Literature, Chaomei Chen, Journal of Data and Information Science, 2, 1-40, 2017
2. Autonomy in materials research: a case study in carbon nanotube growth; P. Nikolaev, D. Hooper, F. Webber, R. Rao, K. Decker, M. Krein, J. Poleski, R. Barto and B. Maruyama, npj Computational Materials, 2, 16031 (2016)
3. Complex structural dynamics of nanocatalysts revealed in Operando conditions by correlated imaging and spectroscopy probes, Y. Li, D. Zakharov, S. Zhao, R. Tappero, U. Jung, A. Elsen, Ph Baumann, Ralph G. Nuzzo, E. A. Stach, and A. I. Frenkel.." Nature Comm., 6, 7583
4. The Structure of Scientific Revolutions, T.S. Kuhn, Chicago: University of Chicago Press, 1962.
5. A Sociological Theory of Scientific Change, S. Fuchs, Social Forces, 71(4), 933-953, 1993.