Stefan Heinzel passed away on 28.08.2018

Stefan Heinzel

We lost the competent leader of the Max Planck Computing and Data Facility, an influential and reliable NOMAD and BiGmax PI. Stefan was a role model of a leader, and it was most impressive how he and his team were running the MPCDF. He was such a wonderful and helpful person and so important for the Max Planck Society. We will miss his friendly and straight personality, his skills, his competent judgement, his reliability, and his sense of obligation.

News

Call for BiGmax participation

How to be part of the BiGmax family

August 2018

BiGmax job offers.


31/08/2017
The electronica blog posted an article about BigMax. Link here.

25/07/2017
The Max Planck Society released a press note about BigMax. The full press release can be found here.

Contact information

Contact us here

BigMax research scope and the participating institutes

 
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 sucessful search of structure-property relationships among multiple lengthscales 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.

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 sucessful search of structure-property relationships among multiple lengthscales 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.
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