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1. Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels

   https://doi.org/10.1088/2399-1984/ac3c8f  

   Zheng, Bowen; Zheng, Zeyu; Gu, Grace X (December 2021, Nano Futures)

   Abstract Graphene aerogels (GAs), a special class of 3D graphene assemblies, are well known for their exceptional combination of high strength, lightweightness, and high porosity. However, due to microstructural randomness, the mechanical properties of GAs are also highly stochastic, an issue that has been observed but insufficiently addressed. In this work, we develop Gaussian process metamodels to not only predict important mechanical properties of GAs but also quantify their uncertainties. Using the molecular dynamics simulation technique, GAs are assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to deduce mechanical properties. Results show that given the same density, mechanical properties such as the Young’s modulus and the ultimate tensile strength can vary substantially. Treating density, Young’s modulus, and ultimate tensile strength as functions of the inclusion size, and using the simulated GA results as training data, we build Gaussian process metamodels that can efficiently predict the properties of unseen GAs. In addition, statistically valid confidence intervals centered around the predictions are established. This metamodel approach is particularly beneficial when the data acquisition requires expensive experiments or computation, which is the case for GA simulations. The present research quantifies the uncertain mechanical properties of GAs, which may shed light on the statistical analysis of novel nanomaterials of a broad variety. 

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2. Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

   https://doi.org/10.1073/pnas.2415068122  

   Winetrout, Jordan J; Li, Zilu; Zhao, Qi; Gaber, Landon; Unnikrishnan, Vinu; Varshney, Vikas; Xu, Yanxun; Wang, Yusu; Heinz, Hendrik (March 2025, Proceedings of the National Academy of Sciences)

   Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts. 

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3. Effects of Graphene Quantum Dots on Thermal Properties of Epoxy Using Molecular Dynamics

   https://doi.org/10.3390/applnano6030015  

   Bamane, Swapnil S; Keles, Ozgur (September 2025, Applied Nano)

   Polymer matrix composites (PMCs) are crucial for their applications in aerospace, electronics, defense, and structural materials. PMCs reinforced with nanofillers offer substantial potential for enhanced thermal and mechanical performance. Although there have been significant developments in nanofiller-based high-performance composites involving graphene, carbon nanotubes, and metal oxides, the smallest of all the fillers, the graphene quantum dot (GQD), has not been explored thoroughly. The objective of this study is to investigate the effects of GQDs on the thermal properties of epoxy nanocomposites using all-atom molecular dynamics (MD) simulations. Specifically, the influence of GQDs on the glass transition temperature (Tg) and coefficient of linear thermal expansion (CTE) of the bisphenol F epoxy is evaluated. Further, the effects of surface functionalization and edge functionalization of GQDs are analyzed. Results demonstrate that the inclusion of functionalized GQDs leads to a 16% improvement in Tg, attributed to enhanced interfacial interactions and restricted molecular mobility in the epoxy network. MD simulations reveal that functional groups on GQDs form strong physical and chemical interactions with the polymer matrix, effectively altering its dynamics at the Tg. These results provide key molecular-level insights into the design of the next generation of thermally stable epoxy nanocomposites for high-performance applications in aerospace and defense. 

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4. Probing phonon focusing, thermomechanical behavior, and moiré patterns in van der Waals architectures using surface acoustic waves

   https://doi.org/10.1038/s41524-024-01315-5  

   Baradwaj, Nitish; Aditya, Anikeya; Mishra, Ankit; Burns, Kory; Lang, Eric; Hachtel, Jordan A; Hattar, Khalid; Aitkaliyeva, Assel; Nakano, Aiichiro; Vashishta, Priya; et al (December 2024, npj Computational Materials)

   Abstract Surface acoustic waves (SAWs) propagate along solid-air, solid-liquid, and solid-solid interfaces. Their characteristics depend on the elastic properties of the solid. Combining transmission electron microscopy (TEM) experiments with molecular dynamics (MD) simulations, we probe atomic environments around intrinsic defects that generate SAWs in vertically stacked two-dimensional (2D) bilayers of MoS₂. Our joint experimental-simulation study provides insights into SAW-induced structural and dynamical changes and thermomechanical responses of MoS₂bilayers. Using MD simulations, we compute mechanical properties from the SAW velocity and thermal conductivity from thermal diffusion of SAWs. The results for Young’s modulus and thermal conductivity of an MoS₂monolayer are in good agreement with experiments. The presence of defects, such as nanopores which generate SAWs, reduces the thermal conductivity of 2D-MoS₂by an order of magnitude. We also observe dramatic changes in moiré patterns, phonon focusing, and cuspidal structures on 2D-MoS₂layers. 

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5. Engineer Energy Dissipation in 3D Graphene Nanolattice Via Reversible Snap-Through Instability

   https://doi.org/10.1115/1.4045544  

   Ni, Bo; Gao, Huajian (March 2020, Journal of Applied Mechanics)

   Abstract Carbon micro/nanolattice materials, defined as three-dimensional (3D) architected metamaterials made of micro/nanoscale carbon constituents, have demonstrated exceptional mechanical properties, including ultrahigh specific strength, stiffness, and extensive deformability through experiments and simulations. The ductility of these carbon micro/nanolattices is also important for robust performance. In this work, we present a novel design of using reversible snap-through instability to engineer energy dissipation in 3D graphene nanolattices. Inspired by the shell structure of flexible straws, we construct a type of graphene counterpart via topological design and demonstrate its associated snap-through instability through molecular dynamics (MD) simulations. One-dimensional (1D) straw-like carbon nanotube (SCNT) and 3D graphene nanolattices are constructed from a unit cell. These graphene nanolattices possess multiple stable states and are elastically reconfigurable. A theoretical model of the 1D bi-stable element chain is adopted to understand the collective deformation behavior of the nanolattice. Reversible pseudoplastic behavior with a finite hysteresis loop is predicted and further validated via MD. Enhanced by these novel energy dissipation mechanisms, the 3D graphene nanolattice shows good tolerance of crack-like flaws and is predicted to approach a specific energy dissipation of 233 kJ/kg in a loading cycle with no permanent damage (one order higher than the energy absorbed by carbon steel at failure, 16 kJ/kg). This study provides a novel mechanism for 3D carbon nanolattice to dissipate energy with no accumulative damage and improve resistance to fracture, broadening the promising application of 3D carbon in energy absorption and programmable materials. 

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