Experimental confirmation of computer-aided polymer blend designs

Brunswick, Andre; Cavanaugh, Timothy J.; Mathur, D.; Russo, A. Peter; Nauman, E. Bruce

doi:10.1002/(sici)1097-4628(19980411)68:2<339::aid-app16>3.0.co;2-s

Cited by 10 publications

(4 citation statements)

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“…The numerical stability of Cahn-Hilliard solution methods is a known issue, especially when using the Flory-Huggins free-energy function at higher χ ij values (Brunswick et al, 1998). Three possible simulation states were identified as shown in Table 1: simstate demonstrating the impact of numerical issues on the solution of the physical model, either the simulation would diverge prematurely due to numerical instability (State 3a), or even though the input parameters were selected such that the physical system is in the chemical spinodal, no demixing would occur (State 2).…”

Section: Polymer Demixing Simulation Resultsmentioning

confidence: 99%

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

Inguva

Mason

Pan

et al. 2020

DCE

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Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.

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Section: Polymer Demixing Simulation Resultsmentioning

confidence: 99%

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

Inguva

Mason

Pan

et al. 2020

DCE

View full text Add to dashboard Cite

show abstract

“…The numerical stability of Cahn-Hilliard solution methods is a known issue, especially when using the Flory-Huggins free-energy function at higher χ i j values (Brunswick et al, 1998). Three possible simulation states were identified as shown in table 1, demonstrating the impact of numerical issues on the solution of the physical model: either the simulation would diverge prematurely due to numerical instability (State 3a) or even though the input parameters were selected such that the physical system is in the chemical spinodal, no demixing would occur (State 2).…”

Section: Polymer Demixing Simulation Resultsmentioning

confidence: 99%

Numerical simulation, clustering and prediction of multi-component polymer precipitation

Inguva¹,

Mason²,

Pan³

et al. 2020

Preprint

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Multi-component polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn-Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning techniques for clustering and consequent prediction of the simulated polymer blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyse the resulting morphology clusters. Supervised machine learning using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but machine learning techniques were able to predict the morphology of polymer blends with ≥90 % accuracy. Impact StatementBy providing predictive tools to polymer engineers that assist in predicting morphological features from easily knowable (or measurable) input parameters, the need to perform time-consuming and costly experiments to obtain desired morphological behaviours in such complex systems is reduced. This could reduce the overall complexity of the Research and Development (R&D) process for a variety of industries. Applying data-driven approaches to analysing simulation data may also help to identify trends and features that are important but might not otherwise be detected by humans.

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“…Beyond the problem of the accurate estimation of Tg, other polymer properties relevant to their end-use performance have been the focus of developers of predictive models. Among these, the prediction of three dimensional polymer structures with exceptional mechanical properties [8], qualitative and quantitative predictions of composition of multicomponent bioglasses [9], the computer-aided design of polymer blends [10], and polymer-receptor interactions in biosensors [11] are worth mentioning. Surprisingly, the prediction of protein adsorption onto polymer surfaces, cell attachment, and cell proliferation (collectively referred to in this article as the "bioresponse") has thus far not been extensively modeled.…”

Section: Prediction Of Polymer Materials Propertiesmentioning

confidence: 99%

Prediction of fibrinogen adsorption for biodegradable polymers: Integration of molecular dynamics and surrogate modeling

Gubskaya

Kholodovych

Knight

et al. 2007

Polymer

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This work is a part of a series of publications devoted to the development of surrogate (semiempirical) models for the prediction of fibrinogen adsorption onto polymer surfaces. Since fibrinogen is one of the key proteins involved in platelet activation and the formation of thrombosis, the modeling of fibrinogen adsorption on the surface of blood contacting medical devices is of high theoretical and practical significance. We report here, for the first time, on the incorporation three-dimensional structures of polymers obtained from atomistic simulations into conventional mesoscopic-scale calculations. Low energy conformations derived from Molecular Dynamics simulations for 45 representatives of a combinatorial library of polyarylates were used in an improved modeling procedure (referred to as "3D surrogate model") instead of simplistic two-dimensional representations of polymer structures, which were used in several previous models (collectively referred to as "2D surrogate models"). In the framework of this 3D model we created 12 model sets of polymers to account for their chirality, conformational diversity and the structural influence of a solvent. For each polymer set, three-dimensional molecular descriptors were generated and then ranked with respect to the experimental fibrinogen adsorption data by means of a Monte Carlo Decision Tree. The most significant descriptors identified by Decision Tree and the experimental dataset were utilized to predict fibrinogen adsorption using an Artificial Neural Network (ANN). The best prediction achieved by the 3D surrogate model demonstrated a noticeable improvement in the predictive quality as compared to the previously used 2D model (as evidenced by the increase in the average Pearson correlation coefficient from 0.67±0.13 to 0.54±0.12). The predictive quality of the 3D surrogate model compares favorably with the best results previously reported for extended 2D model that combines an ANN with Partial Least Squares (PLS) regression and principal component (PC) analysis. The significance of the newly developed 3D model is that it allows high accuracy prediction of fibrinogen adsorption without the need for experimentally derived descriptors and it has better predictive quality than the original 2D surrogate model due to utilization of realistic polymer representations.

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Experimental confirmation of computer-aided polymer blend designs

Cited by 10 publications

References 0 publications

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

Numerical simulation, clustering and prediction of multi-component polymer precipitation

Prediction of fibrinogen adsorption for biodegradable polymers: Integration of molecular dynamics and surrogate modeling

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