From Data to Knowledge: Chemical Data Management, Data Mining and Modelling in Polymer Science

Adams, Nico; Schubert, Ulrich S.

Abstract:

In the modern academic and industrial environment, which is characterized by continuously shortening lifetimes of knowledge and products, the advent of combinatorial chemistry and high-throughput experimentation has profoundly changed the area of compound discovery and characterization. Both synthesis and analysis can be carried out on much reduced time scales, and sample throughput is usually high. One of the consequences of the increase in “discovery activity” is an explosion of experimental data that needs to be organized, administered, stored, and evaluated. This is particularly true in the area of polymer chemistry, where the number of parameters that can be varied during synthesis, formulation, and processing (e.g., monomers, initiators, monomer/initiator ratio, concentrations, temperatures, pressures, additives, stabilizers, etc.) is extremely large. Additionally, there is an extensive amount of characterization and screening data, originating from both classical polymer analysis (Tg, Tm, Mn, Mw, polydispersities) and other materials analytical techniques (conductivity, elasticity, hardness, blend formulations, etc.). The need for computational tools has given rise to the field of “cheminformatics”, which has two main functions, namely, the administration of data and the aiding of data comprehension (i.e., data mining and modeling).

Year:

2004

Type of Publication:

Article

Journal:

Journal of Combinatorial Chemistry

Volume:

6

Pages:

12 – 23