A Data-Centric Approach for Accelerating the Design of Future Nanostructured Polymers and Composites Systems
Our objective is to formulate a novel, data-centric approach to accelerate the development of next-generation nanostructured polymers with unprecedented and predictable combinations of properties. To meet this objective, we are developing: a multiscale modeling strategy that bridges length scales by using machine learning approaches, physics based models, and a robust interphase model built using curated and custom generated data, as well as materials design methods and tools validated through test cases. Data and tools from the work are housed in an open data resource (NanoMine). Our approach advances materials design principles, demonstrated across a broad class of nano-reinforced polymeric composites.
Publications
FAIR and Interactive Data Graphics from a Scientific Knowledge Graph
M. E. Deagen, J. P. McCusker, T. Fateye, S. Stouffer, L. C. Brinson, D. L. McGuinness, and L. S. Schadler
5/27/2022
Data Centric Design: A New Approach to Design of Microstructural Material Systems
W. Chen, A. Iyer, and R. Bostanabad
3/1/2022
Direct evidence of interfacial crystallization preventing weld formation during fused filament fabrication of poly(ether ether ketone)
D. W. Collinson, N. von Windheim, K. Gall, and L. C. Brinson
3/1/2022
The materials tetrahedron has a “digital twin”
M. E. Deagen, L. C. Brinson, R. A. Vaia, and L. S. Schadler
2/1/2022
Metalized polyamide heterostructure as a moisture-responsive actuator for multimodal adaptive personal heat management
X. Li, B. Ma, J. Dai, C. Sui, D. Pande, D. R. Smith, L. C. Brinson, and P. Hsu
12/17/2021
Radiation-Responsive Amino Acid Nanosensor Gel (RANG) for Radiotherapy Monitoring and Trauma Care
K. Pushpavanam, S. Dutta, N. Zhang, T. Ratcliff, T. Bista, T. Sokolowski, E. Boshoven, S. Sapareto, C. M. Breneman, and K. Rege
8/13/2021
Best practices and recommendations for accurate nanomechanical characterization of heterogeneous polymer systems with atomic force microscopy
D. W. Collinson, R. J. Sheridan, M. J. Palmeri, and L. C. Brinson
8/1/2021
Dielectric properties of polymer nanocomposite interphases from electrostatic force microscopy using machine learning
P. Gupta, L. S. Schadler, and R. Sundararaman
3/1/2021
Featureless adaptive optimization accelerates functional electronic materials design
Y. Wang, A. Iyer, W. Chen, and J. M. Rondinelli
11/6/2020
Polymer nanodielectrics—Short history and future perspective
L. S. Schadler, and J. K. Nelson
9/25/2020
Polymer Nanocomposite Data: Curation, Frameworks, Access, and Potential for Discovery and Design
L. C. Brinson, M. Deagen, W. Chen, J. McCusker, D. L. McGuinness, L. S. Schadler, M. Palmeri, U. Ghumman, A. Lin, and B. Hu
7/14/2020
Investigating the effect of surface modification on the dispersion process of polymer nanocomposites
A. S. Prasad, Y. Wang, X. Li, A. Iyer, W. Chen, L. C. Brinson, and L. S. Schadler
7/2/2020
Vanishing Cantilever Calibration Error with Magic Ratio Atomic Force Microscopy
R. J. Sheridan, D. W. Collinson, and L. C. Brinson
6/22/2020
A perspective on the data-driven design of polymer nanodielectrics
L. S. Schadler, W. Chen, L. C. Brinson, R. Sundararaman, P. Gupta, P. Prabhune, A. Iyer, Y. Wang, and A. Shandilya
6/9/2020
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
Y. Zhang, D. W. Apley, and W. Chen
3/18/2020
First-principles identification of localized trap states in polymer nanocomposite interfaces
A. Shandilya, L. S. Schadler, and R. Sundararaman
2/3/2020
Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
G. Chen, Z. Shen, A. Iyer, U. F. Ghumman, S. Tang, J. Bi, W. Chen, and Y. Li
1/8/2020
Data centric nanocomposites design via mixed-variable Bayesian optimization
A. Iyer, Y. Zhang, A. Prasad, P. Gupta, S. Tao, Y. Wang, P. Prabhune, L. S. Schadler, L. C. Brinson, and W. Chen
1/1/2020
Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks
Y. Wang, M. Zhang, A. Lin, A. Iyer, A. S. Prasad, X. Li, Y. Zhang, L. S. Schadler, W. Chen, and L. C. Brinson
1/1/2020
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors
Y. Zhang, S. Tao, W. Chen, and D. W. Apley
8/19/2019
Rethinking interphase representations for modeling viscoelastic properties for polymer nanocomposites
X. Li, Y. Wang, M. Zhang, A. Prasad, W. Chen, L. Schadler, and L. C. Brinson
6/1/2019
NanoMine schema: An extensible data representation for polymer nanocomposites
H. Zhao, Y. Wang, A. Lin, B. Hu, R. Yan, J. McCusker, W. Chen, D. L. McGuinness, L. Schadler, and L. C. Brinson
11/1/2018
Microstructural Materials Design Via Deep Adversarial Learning Methodology
Z. Yang, X. Li, L. Catherine Brinson, A. N. Choudhary, W. Chen, and A. Agrawal
10/1/2018
View All Publications
Research Highlights
Data Centric Nanocomposite Design via Mixed-variable Bayesian Optimization
L. C. Brinson (Duke U.), W. Chen (Northwestern U.), L. Schadler (U. VT)