@article{ART003342728},
author={Ramdas Prasanna Venkatesh and Ganapathy Venkatesan and Palanivel Anand and Manoharan Shunmugasundaram},
title={Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations},
journal={Carbon Letters},
issn={1976-4251},
year={2026},
volume={36},
number={2},
pages={937-956},
doi={10.1007/s42823-026-01040-7}
TY - JOUR
AU - Ramdas Prasanna Venkatesh
AU - Ganapathy Venkatesan
AU - Palanivel Anand
AU - Manoharan Shunmugasundaram
TI - Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations
JO - Carbon Letters
PY - 2026
VL - 36
IS - 2
PB - Korean Carbon Society
SP - 937
EP - 956
SN - 1976-4251
AB - Graphene-reinforced aluminum (Gr/Al) nanocomposites offer exceptional mechanical properties for aerospace, automotive, and electronics applications. Precise estimation of their characteristics, including ultimate tensile strength (UTS) and Young’s modulus (YM), remains challenging due to complex atomic interactions and computational limitations of traditional methods. This study proposes a novel machine learning framework combining Molecular Dynamics (MD) simulations, Adaptive Fast Desensitized Kalman Filter (AFDKF), Diffusion Variational Graph Neural Network (DV-GNN), and Arctic Tern Optimizer (ATO) for efficient and accurate mechanical property prediction. Important variables such as graphene alignment, volume fraction, chirality, and ambient temperature are captured by the method. DV-GNN achieves a prediction accuracy of 99.9%, significantly outperforming existing ML models. The framework also demonstrates low error rates, fast computation, and scalability, providing a robust computational tool for intelligent design of high-strength, lightweight Gr/Al nanocomposites.
KW - Graphene-reinforced aluminum Diffusion variational graph neural network Adaptive fast desensitized kalman filter Arctic tern optimizer Molecular dynamics
DO - 10.1007/s42823-026-01040-7
ER -
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand and Manoharan Shunmugasundaram. (2026). Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations. Carbon Letters, 36(2), 937-956.
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand and Manoharan Shunmugasundaram. 2026, "Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations", Carbon Letters, vol.36, no.2 pp.937-956. Available from: doi:10.1007/s42823-026-01040-7
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand, Manoharan Shunmugasundaram "Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations" Carbon Letters 36.2 pp.937-956 (2026) : 937.
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand, Manoharan Shunmugasundaram. Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations. 2026; 36(2), 937-956. Available from: doi:10.1007/s42823-026-01040-7
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand and Manoharan Shunmugasundaram. "Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations" Carbon Letters 36, no.2 (2026) : 937-956.doi: 10.1007/s42823-026-01040-7
Ramdas Prasanna Venkatesh; Ganapathy Venkatesan; Palanivel Anand; Manoharan Shunmugasundaram. Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations. Carbon Letters, 36(2), 937-956. doi: 10.1007/s42823-026-01040-7
Ramdas Prasanna Venkatesh; Ganapathy Venkatesan; Palanivel Anand; Manoharan Shunmugasundaram. Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations. Carbon Letters. 2026; 36(2) 937-956. doi: 10.1007/s42823-026-01040-7
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand, Manoharan Shunmugasundaram. Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations. 2026; 36(2), 937-956. Available from: doi:10.1007/s42823-026-01040-7
Ramdas Prasanna Venkatesh, Ganapathy Venkatesan, Palanivel Anand and Manoharan Shunmugasundaram. "Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations" Carbon Letters 36, no.2 (2026) : 937-956.doi: 10.1007/s42823-026-01040-7
