Scope

Materials science is entering an era where the growth of data from experiments and simulations is expanding beyond a level that is addressable by established scientific methods. The so-called “4 V challenge” – concerning Volume (the amount of data), Variety (the heterogeneity of form and meaning of data), Velocity (the rate at which data may change or new data arrive), and Veracity (uncertainty of quality) is clearly becoming eminent. Issues are, for example, an early discrimination between valuable and irrelevant experimental data, understanding errors in both experiment and theory, and assigning error bars and trust levels to density-functional theory high-throughput screening results, just to name a few.

Big Data of materials science, however, can also be seen as a chance, promising completely new insight and knowledge gain when fully exploiting the information content in the already available and strongly increasing data. This exploitation requires new and dedicated technology based on approaches in statistical and machine learning, compressed sensing, and other recent technologies from mathematics, computer science, statistics, and information technology. Making use of this synergy, will enable the development of novel, domain-specific, and even property-specific methods to enter and shape the era of data-driven materials research