Modeling and simulations for 2D materials: a ReaxFF perspective

N Nayir, Q Mao, T Wang, M Kowalik, Y Zhang… - 2D …, 2023 - iopscience.iop.org
Recent advancements in the field of two-dimensional (2D) materials have led to the
discovery of a wide range of 2D materials with intriguing properties. Atomistic-scale …

Deep learning in two-dimensional materials: Characterization, prediction, and design

X Meng, C Qin, X Liang, G Zhang, R Chen, J Hu… - Frontiers of …, 2024 - Springer
Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing
interest because of their excellent chemical and physical properties, as well as promising …

[HTML][HTML] An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions

Z Zhang, Y Ao, S Li, GX Gu - Theoretical and Applied Mechanics Letters, 2024 - Elsevier
Conventional wing aerodynamic optimization processes can be time-consuming and
imprecise due to the complexity of versatile flight missions. Plenty of existing literature has …

Decoding the origins of strength anisotropy in two-dimensional materials

G Zhang, S Liu, H Qin, Y Liu - International Journal of Solids and Structures, 2024 - Elsevier
Defects are inevitable in two-dimensional (2D) materials, which is widely recognized to
affect the strength of 2D materials. It is known the uniaxial tension strength is significantly …

Machine learning enabled optimization of showerhead design for semiconductor deposition process

Z **, DD Lim, X Zhao, M Mamunuru, S Roham… - Journal of Intelligent …, 2024 - Springer
In semiconductor fabrication, the deposition process generates layers of materials to realize
insulating and conducting functionality. The uniformity of the deposited thin film layers' …

Fracture strength of Graphene at high temperatures: data driven investigations supported by MD and analytical approaches

S Varma Siruvuri, H Verma, B Javvaji… - International Journal of …, 2022 - Springer
The extraordinary opto-electronic and mechanical properties of Graphene makes it a
popular material for several applications. However, defects like: cracks, and voids are …

[HTML][HTML] Leveraging graph neural networks and neural operator techniques for high-fidelity mesh-based physics simulations

Z **, B Zheng, C Kim, GX Gu - APL Machine Learning, 2023 - pubs.aip.org
Develo** fast and accurate computational models to simulate intricate physical
phenomena has been a persistent research challenge. Recent studies have demonstrated …

[HTML][HTML] Residual Pyramidal GAN (RP-GAN) for crack detection and prediction of crack growth in engineered cementitious composites

GE Amieghemen, M Ramezani, MM Sherif - Measurement, 2025 - Elsevier
Crack detection has recently gained credence in the field of engineering automation.
Engineered cementitious composites (ECC) are a durable, and environmentally friendly …

Fracture at the two-dimensional limit

B Ni, D Steinbach, Z Yang, A Lew, B Zhang, Q Fang… - Mrs Bulletin, 2022 - Springer
More than a century ago, AA Griffith published the seminal paper establishing the
foundational framework for fracture mechanics. The elegant theory creatively introduced the …

Machine learning and experiments: A synergy for the development of functional materials

B Zheng, Z **, G Hu, J Gu, SY Yu, JH Lee, GX Gu - MRS Bulletin, 2023 - Springer
With machine learning (ML) and artificial intelligence (AI) becoming increasingly refined and
accessible, computer engineers and materials scientists are utilizing these data-driven …