Exploiting Big Data to Create Innovative Materials
New Paradigm in the Materials Sciences
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. 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.
"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. 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.
Patterns in Large Data Volumes Reveal Completely New Information
Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, explains that procedures such as x-ray structural analysis or atom probe tomography alone deliver millions of data values per minute; data from which researchers gain insights into the configuration of atoms in solids, for example. Enormous data volumes also result from the quantum mechanics analyses commonplace in solid-state physics and chemistry. The researchers can now draw conclusions on material properties from these data.
However, the new alliance aims to gain even more insights from these data. New methods will be developed to this end, and existing methods refined. "For example, in materials research the data present highly specific challenges to the computer algorithms", explains Benner, who coordinates the new collaboration together with Matthias Scheffler. One of the central objectives: investigating the data for particular structures or patterns, which will then allow completely new information to be extracted, in addition to what is already known.
he cooperating Max Planck scientists are consequently now hopeful that in future, materials researchers can gain new insights from their existing data material. The network aims to concentrate joint activities on five different topics. The objective is to be able to theoretically predict the properties of metals and alloys, determine the causal relationships between material properties and data structures, develop data diagnostics methodologies to convert collected experimental data even more quickly to image information, and facilitate the design of polymer materials with specific, desired properties. In the fifth topic area, the network aims to continue the already started Materials Encyclopaedia. The Novel Materials Discovery Laboratory (NOMAD Centre of Excellence) had previously worked on this encyclopaedia, using exclusively theoretically computed entries. Experimental data will now also be included as part of BigMax.
Until the dream of the multi-dimensional material map is fulfilled, in which one simply looks up the best material to use, there is still a long way to go. But Matthias Scheffler does not doubt the fact that Big Data will help reach this target. Here, he sees a new paradigm in the materials sciences: "Previously, researchers have investigated selected systems and developed models based on a general theoretical understanding", says Scheffler. "I believe that the future quest in terms of Big Data analyses will be the search for structures and patterns in large data volumes. And once we have finally developed the equations to describe them, we can then apply them to materials that we have not even analyzed yet."
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), Colloids and Interfaces (Potsdam-Golm), Microstructure Physics (Halle), Polymer Research (Mainz), Eisenforschung GmbH (Düsseldorf), Biogeochemistry (Jena), Physics of Complex Systems (Dresden), Structure and Dynamics of Matter (Hamburg), Intelligent Systems (Tübingen) and Informatics (Saarbrücken), and the Max Planck Computing and Data Facility (Garching)