Polymerization Data Mining: A Perspective. Issue 4 (20th December 2018) - Mohammadi, Yousef; Penlidis, Alexander
Abstract: Polymerization data mining is the art of revealing insights and developing new knowledge from huge amounts of data routinely generated in polymerization systems and polymer characterization (polymerization processes and properties of polymer materials are the specific topic of this article). This becomes possible via development and implementation of robust and versatile intelligent data classifiers/clusterers for precise (numerical) processing of any given large theoretical/experimental datasets. Data mining is capable of effectively "cracking" recipe–microstructure–property interrelationships in modern macromolecular reaction engineering. This work offers a perspective, which contains a brief overview of the current state‐of‐the‐art and history of the area, along with current developments and trends in the data mining field (for polymerizations) with several conceptual examples. All in all, and similar to what is happening in other areas, polymerization data mining is becoming a necessity. The first applications seem promising. Applying molecular simulation approaches and artificial intelligence techniques, the design and establishment of powerful simulators for characterization and processing of virtually synthesized macromolecules are open to future developments, being of paramount importance to both industry and academia. Abstract : Polymerization data mining is the art of discovering insights/knowledge from huge amounts of data routinely generated in polymerization systems and polymer characterization. This becomes possible via the development and implementation of robust and versatile intelligent data classifiers/clusterers for precise processing of any given large theoretical/experimental datasets. Data mining is capable of effectively "cracking" recipe–microstructure–property interrelationships in modern macromolecular reaction engineering.