Drowning in Data — A Viewpoint on Strategies for Doing Science with Simulations

Abstract

Computational materials scientists are nowadays capable of producing an enormous wealth of simulation data. When analyzing such predictions the challenge often consists in extracting meaningful observations from them, and, wherever possible, to discover general and representative principles behind the often-huge data sets. Only the capability of condensing large data sets into the discovery of new microstructure principles renders materials simulations into computational materials science. This chapter is devoted to this topic. The following sections present some strategies for filtering new observations from materials simulations.