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Polymers
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Simulation and Calculation of Polymer Composite Materials
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Simulation and Calculation of Polymer Composite Materials

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".

Deadline for manuscript submissions: 5 May 2025 | Viewed by 5569

Special Issue Editors

Dr. Han Liu

E-Mail Website
Guest Editor
1. SOlids inFormaTics AI-Laboratory (SOFT-AI-Lab), Sichuan University, Chengdu 610065, China
2. College of Polymer Science and Engineering, Sichuan University, Chengdu 610065, China
Interests: computational materials; machine learning; molecular dynamics; multiscale modeling; inverse design; disordered solids; polymer composites; glassy materials; porous materials; mechanical metamaterials
Dr. Maja Kuzmanović

E-Mail Website
Co-Guest Editor
College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
Interests: polymer composites; fibre-reinforced composites; processing–structure-property relationships; polymer rheology; crystalline structure; morphology development; mechanical properties; advance manufacturing
Dr. Zhenhua Wei

E-Mail Website
Co-Guest Editor
Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: composite materials; polymer encapsulation; polymer-reinforced concrete; thermal regulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to their disordered out-of-equilibrium nature, polymeric solids exhibit a complex structure–property relationship that challenges the rational design of polymer materials and their products with tailored properties. By adding extra phases into the polymeric matrix, polymer composites feature an increasing extent of structural complexity, which, in turn, significantly enhances the tunability of their properties but renders their rational design even harder. With the recent advances in computational materials science, physics- and data-driven modeling—including simulations and machine learning—offer an attractive opportunity to revisit these challenges facing polymer composite design.
In this Special Issue, we invite contributions that address several aspects pertaining to the modelling and inverse design of polymer composites, including those that decipher the structure–property relationship of polymeric solids by conventional modeling tools (such as molecular dynamics simulations and finite element analysis), develop new simulation schemes to predict polymer properties by simplifying the underlying physics, build surrogate simulation engines of polymer composites by machine learning, and combine high-throughput simulations and machine learning to accelerate the discovery of novel polymer materials. More broadly, any original contributions (including reviews) relevant to rationalizing computational modeling of polymer composites and their inverse design are welcome. We hope that this Special Issue will modestly help to stimulate new developments in that direction.

Dr. Han Liu
Guest Editor

Dr. Maja Kuzmanović
Dr. Zhenhua Wei
Co-Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • polymer composites
  • polymeric solids
  • mechanical property
  • structural morphology
  • molecular dynamics simulation
  • finite element analysis
  • multiscale modeling
  • constitutive modeling
  • machine learning
  • inverse design

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Published Papers (4 papers)

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Research

20 pages, 8363 KiB  
Article
Predicting Stress–Strain Curve with Confidence: Balance Between Data Minimization and Uncertainty Quantification by a Dual Bayesian Model
by Tianyi Li, Zhengyuan Chen, Zhen Zhang, Zhenhua Wei, Gan-Ji Zhong, Zhong-Ming Li and Han Liu
Polymers 2025, 17(4), 550; https://doi.org/10.3390/polym17040550 - 19 Feb 2025
Viewed by 495
Abstract
Driven by polymer processing–property data, machine learning (ML) presents an efficient paradigm in predicting the stress–strain curve. However, it is generally challenged by (i) the deficiency of training data, (ii) the one-to-many issue of processing–property relationship (i.e., aleatoric uncertainty), and (iii) the unawareness [...] Read more.
Driven by polymer processing–property data, machine learning (ML) presents an efficient paradigm in predicting the stress–strain curve. However, it is generally challenged by (i) the deficiency of training data, (ii) the one-to-many issue of processing–property relationship (i.e., aleatoric uncertainty), and (iii) the unawareness of model uncertainty (i.e., epistemic uncertainty). Here, leveraging a Bayesian neural network (BNN) and a recently proposed dual-architected model for curve prediction, we introduce a dual Bayesian model that enables accurate prediction of the stress–strain curve while distinguishing between aleatoric and epistemic uncertainty at each processing condition. The model is trained using a Taguchi array dataset that minimizes the data size while maximizing the representativeness of 27 samples in a 4D processing parameter space, significantly reducing data requirements. By incorporating hidden layers and output-distribution layers, the model quantifies both aleatoric and epistemic uncertainty, aligning with experimental data fluctuations, and provides a 95% confidence interval for stress–strain predictions at each processing condition. Overall, this study establishes an uncertainty-aware framework for curve property prediction with reliable, modest uncertainty at a small data size, thus balancing data minimization and uncertainty quantification. Full article
(This article belongs to the Special Issue Simulation and Calculation of Polymer Composite Materials)
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20 pages, 13685 KiB  
Article
Impact of Bond–Slip Models on Debonding Behavior in Strengthened RC Slabs Using Recycled Waste Fishing Net Sheets
by Huy Q. Nguyen, Taek Hee Han, Jun Kil Park and Jung J. Kim
Polymers 2024, 16(21), 3093; https://doi.org/10.3390/polym16213093 - 1 Nov 2024
Cited by 1 | Viewed by 1294
Abstract
This study investigated the performance of recycled waste fishing net sheets (WSs) as a sustainable strengthening material for reinforced concrete (RC) slabs. The primary challenge addressed is the debonding failure caused by the low bond strength at the WS-to-concrete interface. To analyze this, [...] Read more.
This study investigated the performance of recycled waste fishing net sheets (WSs) as a sustainable strengthening material for reinforced concrete (RC) slabs. The primary challenge addressed is the debonding failure caused by the low bond strength at the WS-to-concrete interface. To analyze this, two full-scale RC slabs—one with and one without strengthening—were cast and tested under a four-point bending setup. Finite element (FE) models incorporating existing bond–slip laws were developed using the ABAQUS software to simulate the strengthened slab’s behavior. A sensitivity analysis was performed to assess the impact of bond–slip parameters on the failure mechanism. Experimental results indicated that the WS-strengthened slab enhanced the RC slab capacities by 15% in yield load and 13% in initial stiffness. Furthermore, the maximum shear stress of 0.5τmax or interfacial fracture energy of 0.2Gf, compared to values proposed by Monti et al., enabled the simulation of the global response observed in the experiment. Full article
(This article belongs to the Special Issue Simulation and Calculation of Polymer Composite Materials)
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18 pages, 8055 KiB  
Article
Study on the Factors Affecting the Self-Healing Performance of Graphene-Modified Asphalt Based on Molecular Dynamics Simulation
by Fei Guo, Xiaoyu Li, Ziran Wang, Yijun Chen and Jinchao Yue
Polymers 2024, 16(17), 2482; https://doi.org/10.3390/polym16172482 - 30 Aug 2024
Cited by 2 | Viewed by 1124
Abstract
To comprehensively understand the impact of various environmental factors on the self-healing process of graphene-modified asphalt, this study employs molecular dynamics simulation methods to investigate the effects of aging degree (unaged, short-term aged, long-term aged), asphalt type (base asphalt, graphene-modified asphalt), healing temperature [...] Read more.
To comprehensively understand the impact of various environmental factors on the self-healing process of graphene-modified asphalt, this study employs molecular dynamics simulation methods to investigate the effects of aging degree (unaged, short-term aged, long-term aged), asphalt type (base asphalt, graphene-modified asphalt), healing temperature (20 °C, 25 °C, 30 °C), and damage degree (5 Å, 10 Å, 15 Å) on the self-healing performance of asphalt. The validity of the established asphalt molecular models was verified based on four physical quantities: density, radial distribution function analysis, glass transition temperature, and cohesive energy density. The simulated healing time for the asphalt crack model was set to 200 ps. The following conclusions were drawn based on the changes in density, mean square displacement, and diffusion coefficient during the simulated healing process under different influencing factors: Dehydrogenation and oxidation of asphalt molecules during the aging process hinder molecular migration within the asphalt crack model, resulting in poorer self-healing performance. As the service life increases, the decline in the healing performance of graphene-modified asphalt is slower than that of base asphalt, indicating that graphene-modified asphalt has stronger anti-aging properties. When the vacuum layer in the asphalt crack model is small, the changes in the diffusion coefficient are less pronounced. As the crack width increases, the influence of various factors on the diffusion coefficient of the asphalt crack model becomes more significant. When the crack width is large, the self-healing effect of asphalt is more dependent on these influencing factors. Damage degree and oxidative aging have a more significant impact on the healing ability of graphene-modified asphalt than healing temperature. Full article
(This article belongs to the Special Issue Simulation and Calculation of Polymer Composite Materials)
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10 pages, 5592 KiB  
Communication
Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning
by Yingnan Yan, Jiliang Du, Shiwei Ren and Mingchao Shao
Polymers 2024, 16(3), 356; https://doi.org/10.3390/polym16030356 - 28 Jan 2024
Cited by 4 | Viewed by 1739
Abstract
Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression [...] Read more.
Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate. Full article
(This article belongs to the Special Issue Simulation and Calculation of Polymer Composite Materials)
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