Key Takeaways & Executive Findings
- •• • The dataset provides the first MD simulation records for CFRTP interfacial shear, using the CHONSi-2024 ReaxFF force field, validated against experimental shear modulus, yield behavior, stress-strain curves, and fracture morphology, ensuring quantitative agreement. • • Two distinct groove models were constructed: minor groove (A1) with hydrogen bond density of 11.06 bonds/nm2 and large groove (A8) with 9.61 bonds/nm2, enabling systematic study of interfacial hydrogen bonding effects on shear deformation. • • The dataset includes full-chain data: atomic trajectories, local structural evolution, interfacial stress-strain responses, and thermodynamic parameters, directly usable for visualization and analysis in mainstream software. • • Structured feature data (local order parameters, atomic stress/strain, coordination environments) serve as ideal training inputs for machine learning force fields and structure-property relationship models, accelerating high-throughput screening of interfacial structures.
Abstract
The first molecular dynamics (MD) simulation dataset is reported for the interfacial shear behavior of carbon fiber/thermoplastic composites (CFRTPs), aimed at overcoming the critical interfacial problem that limits their high-end applications such as aerospace and new energy vehicles. The study features two key advances. First, we use the newly developed CHONSi-2024 reactive force field (ReaxFF), which provides high-precision parameters specifically for CFRTP interfacial systems. Second, the atomic models are constructed based on experimental characterization data and rigorously validated across multiple parameters, including shear modulus, yield behavior, stress-strain curves, and fracture morphology, ensuring quantitative agreement with experimental results. This dataset provides a complete record of the simulations, encompassing atomic trajectories, local structural changes, interfacial stress-strain responses, and system thermodynamic behaviors. These data offer direct atomic-scale insights into the interfacial strengthening mechanisms. The generated trajectories are compatible with mainstream software for visualization and analysis. Moreover, the dataset constitutes a high-quality resource for developing machine learning force fields, building structure-property relationships, and enabling the predictive modeling and high-throughput screening of composite interfaces. This dataset is anticipated to advance the fundamental understanding of composite interfaces and facilitate the rational design of high-performance CFRTPs.
1. Introduction
Carbon fiber-reinforced thermoplastic composites (CFRTPs) are critical for lightweight structural applications in aerospace and new-energy vehicles due to their ultra-high specific strength and stiffness, fatigue resistance, recyclability, and impact toughness. However, their industrial adoption lags behind thermosetting composites because the high melt viscosity of thermoplastic matrices leads to insufficient interfacial wetting, defect formation, and failure to achieve theoretical interfacial shear strength (IFSS). This interfacial bottleneck severely restricts the full realization of their mechanical properties, making interfacial regulation a core research direction.
To address this, we present the first molecular dynamics (MD) simulation dataset specifically for CFRTP interfacial shear behavior, employing the newly developed CHONSi-2024 reactive force field (ReaxFF) that provides high-precision parameters for these systems. Atomic models are constructed from experimental characterization data and rigorously validated against multiple mechanical parameters, ensuring quantitative agreement. This dataset captures full-chain atomic trajectories, local structural changes, stress-strain responses, and thermodynamic behaviors, offering direct atomic-scale insights into interfacial strengthening mechanisms and serving as a high-quality resource for machine learning force field development and predictive modeling.
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Gao Yuzhao, Li Chaochao, Zhang Rui, Ma Yuanyuan, Lu Kuan (2026). A molecular dataset for the shear deformation of thermoplastic structural materials. New Carbon Materials. https://doi.org/10.1016/S1872-5805(26)61099-2
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Frequently Asked Questions
What specific validation metrics were used to ensure the MD simulations quantitatively match experimental results?
The simulations were validated against shear modulus, yield behavior, stress-strain curves, and fracture morphology, all showing quantitative agreement with experimental data. This ensures the dataset's reliability for studying interfacial shear deformation.
How does the hydrogen bond density differ between the two groove models, and what is the significance for interfacial strength?
The minor groove model (A1) has a hydrogen bond density of 11.06 bonds/nm2, while the large groove model (A8) has 9.61 bonds/nm2. This difference allows investigation of how hydrogen bonding at the interface influences shear resistance and failure mechanisms.
What is the CHONSi-2024 ReaxFF force field, and why is it advantageous for CFRTP systems?
CHONSi-2024 is a newly developed reactive force field with high-precision parameters specifically for carbon fiber/thermoplastic interfacial systems. It enables accurate simulation of bond breaking and formation during shear deformation, which is critical for capturing interfacial failure.
How can this dataset be used for machine learning force field development?
The dataset provides structured feature data including local order parameters, atomic stress/strain, and coordination environments, which are ideal inputs for training machine learning force fields. This can accelerate the development of predictive models for composite interface properties.
What are the potential applications of this dataset in high-throughput screening?
The dataset enables high-throughput screening of interfacial structures by providing a benchmark for predicting mechanical properties and identifying weak atomic configurations. This can guide the rational design of high-performance CFRTPs.
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