Materials Informatics: From Data to Knowledge Towards Integrated eScience Approaches

Combinatorial and high-throughput experimentation techniques have had a remarkable impact on the way research is performed in the pharmaceutical industry, medical research, catalyst research and more recently also in polymer and materials research. In the latter two cases, initial efforts in adopting combinatorial and high-throughput methodologies have focused on the development of new hardware or synthetic and formulation methodologies, followed by the elimination of bottlenecks in the characterization (screening) processes. This has resulted in large amounts of data, which need to be handled, administered and stored in a retrievable manner. However, there has been much less development in the area of informatics for materials and polymer science experiments. Even the most sophisticated laboratory screening and discovery process is useless if data generated during experimentation cannot be accessed, mined and acted upon with sufficient speed. That means that any form of modern experimentation (whether "classical" or high-throughput) should be based on the productive interplay of statistical techniques (design-of-experiments), molecular modeling as well as cheminformatics.