Hausdorff Forum June 8, 2018 - 14:15

Location: Lipschitz-Saal, Endenicher Allee 60

14:15 Dierk Raabe (Max-Planck-Institut für Eisenforschung, Düsseldorf): From Seeing Atoms Toward Understanding Atoms: Methods, Results and Challenges of Advanced Atom Probe Tomography

Atom probe tomography allows the imaging and chemical characterization of materials in three dimensions at the atomic scale. One single experiment can provide information on up to 1 billion atoms.
Current challenges lie particularly in obtaining not only the local chemical composition but also structure information at near atomic scale from such experiments of a site specific specimen region.
Different types of approaches are presented to tackle these challenges, placing particular attention on three techniques: These are (i) crystallographic atom probe tomography; (ii) correlative microscopy which works by conducting electron optical observations and atom probe tomography on exactly the same material portion; and (iii) advanced field ion microscopy.
While these techniques enable probing of materials with so far unprecedented precision, the ultimate goal of this endeavor lies in understanding, discovering and designing new materials bottom up from the atomic scale.
It is conceivable that mathematical and machine learning techniques may play a substantial future role in that context since advanced atom probe tomography and the associated correlative imaging techniques establish currently one of the very few areas in materials science where experimentally obtained big data sets are routinely made available.

15:15 - 15:45 Tea

15:45 Matthias Scheffler (Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin-Dahlem und UCSB, Santa Barbara, California): The Fourth Paradigm of Materials Science: Data-Driven versus Physics-Driven Research

The discovery of
• improved or even novel - not just new - materials or
• hitherto-unknown properties of known materials
to meet a specific scientific or industrial requirement is one of the most exciting and economically important applications of high-performance computing to date. In this talk I will discuss methods and applications how to extract knowledge from the resulting deluge of data. Specifically I will describe how to spot yet unseen patterns or structures in the data, by identifying the key atomic and collective physical actuators by compressed sensing and machine learning. This enables us to build maps of materials where different regions correspond to materials with different properties. As the connections between actuators and materials properties are intricate, attempts to describe the relationship in terms of an insightful physical model may be pointless.

I will demonstrate our methodology for describing and predicting 2D topological insulators, the metal/insulator classification, catalytic CO2 activation, and more.

(*) This work was supported by the NOMAD CoE (Novel Materials Discovery Center of Excellence: