Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing–structure–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address:
Materials Informatics and Data System for Polymer Nanocomposites Analysis and Design
The application of Materials Informatics to polymer nanocomposites would result in faster development and commercial implementation of these promising materials, particularly in applications requiring a unique combination of properties. This chapter focuses on a new data resource for nanocomposites — NanoMine — and the tools, models, and algorithms that support data-driven materials design. The chapter begins with a brief introduction to NanoMine, including the data structure and tools available.
Critical to the ability to design nanocomposites, however, is developing robust structure–property–processing (s–p–p) relationships. Central to this development is the choice of appropriate microstructure characterization and reconstruction (MCR) techniques that capture a complex morphology and ultimately build statistically equivalent reconstructed composites for accurate modeling of properties. A wide range of MCR techniques is reviewed followed by an introduction of feature selection and feature extraction techniques to identify the most significant microstructure features for dimension reduction. This is then followed by examples of using a descriptor-based representation to create processing–structure (p–s) and structure–property (s–p) relationships for use in design. To overcome the difficulty in modeling the interphase region surrounding nanofillers, an adaptive sampling approach is presented to inversely determine the inter-phase properties based on both FEM simulations and physical experiment data of bulk properties. Finally, a case study for nanodielectrics in a capacitor is introduced to demonstrate the integration of the p–s and s–p relationships to develop optimized materials for achieving multiple desired properties.
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- Award ID(s):
- 1729452
- NSF-PAR ID:
- 10187213
- Date Published:
- Journal Name:
- Handbook on Big Data and Machine Learning in the Physical Sciences
- Volume:
- 1
- Page Range / eLocation ID:
- 65-126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract https://bisque.ece.ucsb.edu/module_service/Composite_Strength/ ) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments. -
Data-driven methods have attracted increasingly more attention in materials research since the advent of the material genome initiative. The combination of materials science with computer science, statistics, and data-driven methods aims to expediate materials research and applications and can utilize both new and archived research data. In this paper, we present a data driven and deep learning approach that builds a portion of the structure–property relationship for polymer nanocomposites. Analysis of archived experimental data motivates development of a computational model which allows demonstration of the approach and gives flexibility to sufficiently explore a wide range of structures. Taking advantage of microstructure reconstruction methods and finite element simulations, we first explore qualitative relationships between microstructure descriptors and mechanical properties, resulting in new findings regarding the interplay of interphase, volume fraction and dispersion. Then we present a novel deep learning approach that combines convolutional neural networks with multi-task learning for building quantitative correlations between microstructures and property values. The performance of the model is compared with other state-of-the-art strategies including two-point statistics and structure descriptor-based approaches. Lastly, the interpretation of the deep learning model is investigated to show that the model is able to capture physical understandings while learning.more » « less
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Solid‐state shear pulverization (SSSP) is a continuous polymer processing methodology based on a modified twin‐screw extruder. The unique application of low barrel temperatures and mechanochemistry has contributed to the development of an extensive range of polymer‐based materials, from environmentally responsible polymer blends to nanocomposites, for more than 30 years. The complex processing‐structure‐processing relationships in SSSP can be elucidated by way of integrated, measured covariants that capture the interplay between numerous processing parameters. Residence time distribution and specific mechanical energy are evaluated in a base case polypropylene (PP) study under a full factorial experiment involving screw design, screw speed, and throughput parameters. These factors are in turn correlated to dispersion morphology and thermal property results from a parallel study based on a model PP/carbon black composite. This investigation highlights the tunability of SSSP processing parameters for tailored output with desired purposes and applications. In particular, enhanced residence distribution can be achieved with low screw speed and high throughput settings, leading to high levels of material mixing and shear. POLYM. ENG. SCI., 60:503–511, 2020. © 2019 Society of Plastics Engineers
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1142/9789811204555_0003
