BiGmax scope

Materials science is entering an era where the growth of data from experiments and calculations is expanding beyond a level that is properly processable by established scientific methods. Dealing with this big data is not just a technical challenge but much more it is a great chance. Big-data analytics will revolutionize new material discovery and will make the successful search of structure-property relationships among multiple length scales and timescales possible.
Thus far, the search for new materials for new applications was limited to educated guesses mostly based on selective experiments. By tackling this big data challenge with high-speed computing  extremely large, disparate databases and large-scale computations have to be dealt with. But recent advances in data mining will allow pattern recognition and pattern prediction in an unprecedented way. The outcome of big-data-driven materials science approaches will then impact the way experiments and data analyses are done.

 Big data on innovative materials

Twelve institutes of the Max Planck Society combine their know-how in data-driven materials science. The aim is a better use of the possibilities associated with analyzing large amounts of data.

Which alloying constituents lend a steel unique bending strength, extreme hardness and non-rusting properties? Are semiconductors that promise greater efficiencies for solar modules available, and do they offer greater flexibility than silicon? What would be the best catalyst for a very specific chemical reaction? Or, How should a surface be coated to achieve the best possible thermal protection? To more easily find answers to these typical problems facing materials scientist in future, researchers from eleven Max Planck Society facilities hope to better exploit the opportunities presented by analyzing large volumes of data. To this end, they cooperate in MaxNet on Big-Data-Driven Materials Science or, simply, BiGmax.

Generally, when scientists search for a new material for a specific purpose, they previously had to rely on the results of experiments on selected materials. And yet they never know whether there is not a better solution out there. How practical would it be, then, if researchers from both academia and industry could simply refer to a table to find the optimal material for their purpose? However, this is still far from the reality. "To date, around 240,000 inorganic materials alone are known; yet we have knowledge of only some of the properties of less than 100 of these substances", says Matthias Scheffler, Director at the Max Planck Society's Fritz Haber Institute in Berlin. As a theoretical physicist, he is certain that the large volumes of data being universally collected, also referred to as Big Data, can help to move closer to the table mentioned above. He imagines though this table more as a kind of multi-dimensional materials map.

Scheffler is a co-initiator of the cross-institutional alliance MaxNet on Big Data-Driven Materials Science within the Max Planck Society. The declared aim of BigMax is to innovatively utilize the large, in part previously existing data, and to thereby make them a driving force in materials research. In addition to the Fritz Haber Institute, another eleven MPG facilities are collaborating: the Max Planck Institutes for Dynamics of Complex Technical Systems (in Magdeburg), for colloid and interface research (Potsdam-Golm), for microstructure physics (Halle), for polymer research (Mainz), for iron research (Düsseldorf), for physics of complex systems (Dresden), for structure and dynamics of matter (Hamburg), for intelligent systems (Tübingen), for computer science (Saarbrücken) and the Max Planck Computing and Data Facility (Garching).

 

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