Extracting physics through deep data analysis

Abstract

In recent decades humankind has become very apt at generating and recording enormous amounts of data, ranging from tweets and selfies on social networks, to financial transactions in banks and stores. The scientific community has not shunned this popular trend and now routinely produces hundreds of petabytes of data per year [1]. This is because materials and phenomena in the world around us exist in an interweaved, entangled form, which gives rise to the complexity of the Universe and determines the size and complexity of the data that describes it. Science and technology endeavor to unravel this convolution and extract pure components from the mixtures, be it in ore mining and metal smelting or separation of thermal conductivity into the electronic and phononic contributions. Decomposition of complex behavior is the key to understanding manifestations of Nature. However, tools to carry out this task are not readily available, and therefore, intricate systems often remain well-characterized experimentally, but still not well understood due to intricacy of the collected data. Lastly, in materials science, understanding and ultimately designing new materials with complex properties will require the ability to integrate and analyze data from multiple instruments, including computational models, designed to probe complementary ranges of space, time, and energy.