Development of Materials Informatics Tools and Infrastructure to Enable High Throughput Materials Design
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Development of Materials Informatics Tools and Infrastructure to Enable High Throughput Materials Design Michael P. Krein1, Bharath Natarajan2, Linda S. Schadler2, L. C. Brinson3, Hua Deng3, Donghai Gai3, Yang Li3, and Curt M. Breneman1* 1 Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, U.S.A. 2 Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, U.S.A. 2 Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road, Room B222, Evanston, IL 60208, U.S.A *To whom correspondence should be addressed ABSTRACT Polymer nanocomposites (PNC) are complex material systems in which the dominant length scales converge. Our approach to understanding nanocomposite tradespace uses Materials Quantitative Structure-Property Relationships (MQSPRs) to relate molecular structures to the polar and dispersive components of corresponding surface tensions. If the polar and dispersive components of surface tensions in the nanofiller and polymer could be determined a priori, then the propensity to aggregate and the change in polymer mobility near the particle could be predicted. Derived energetic parameters such as work of adhesion, work of spreading and the equilibrium wetting angle may then used as input to continuum mechanics approaches that have been shown able to predict the thermomechanical response of nanocomposites and that have been validated by experiment. The informatics approach developed in this work thus enables future in silico nanocomposite design by enabling virtual experiments to be performed on proposed nanocomposite compositions prior to fabrication and testing. INTRODUCTION The polymer nanocomposite literature has demonstrated a myriad of potential structures, chemistries, and self assembled morphologies that could significantly impact commercial and military applications. Given the potential of nanocomposite materials’ properties, there is a strong desire to characterize and understand the tradespace of nanocomposites, the important factors relating nanostructure to materials properties and an effective way to control materials properties at the manufacturing scale. Due to the complexity of the systems and the importance of all length scales, computational approaches to predict macroscopic thermomechanical properties of polymer nanocomposites are currently in their infancy. Existing design approaches rely heavily on trial-and-error learning. Quantitative Structure-Property Relationships (QSPRs) have a long history in chemistry,[1, 2]. In QSPRs, chemical descriptors, numerical representations of chemical structure, are related to properties via statistical models. As they are statistical models based on representations of known data, QSPRs must be validated; care must be taken in understanding the limitations of the known data and when predictions from the models are useful.[3, 4]
Methods that allow accurate prediction of polymer properties have long been sought,[5-9] but th
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