Accurately predicting the mechanical properties of graphene-reinforced
metal matrix composites is of utmost importance due to its critical
role in the design and utilization of nanocomposite materials. The
conventional approach of employing molecular dynamics (MD) simulations
for this purpose faces a substantial increase in computational costs
when considering the combined effects of multiple factors. In contrast,
machine learning (ML) models offer a rapid and efficient alternative
by swiftly comprehending and predicting material properties following
adequate training. In this paper, we employed a long short-term memory
(LSTM) model, based on MD calculation data, to accurately predict
the mechanical response and key mechanical properties of nickel–graphene
composite nanomaterials. Specifically, we thoroughly investigated
the comprehensive impact of temperature, graphene orientation angle,
and graphene volume fraction on the mechanical properties. Our verification
process revealed that high graphene volume and high orientation angles
led to increased dislocation absorption, consequently weakening the
composite material. To assess the hardness prediction capabilities,
we conducted a comparative analysis between the LSTM model and classical
multilayer perceptron (MLP) neural networks, as well as the traditional
nonlinear regression method, support vector machine (SVM). The obtained
results demonstrated that the LSTM models exhibited a remarkable ability
to accurately predict the mechanical properties of nickel–graphene
composite nanomaterials, showcasing Pearson correlation coefficients
exceeding 0.95 when compared to the calculation data. Moreover, the
LSTM model effectively comprehends and predicts the complete indentation
depth–force curve, thus providing enhanced predictions of material
properties. This study proposes an innovative combination of MD simulations
and ML models, which holds significant application potential in predicting
and designing the performance of graphene-reinforced metal matrix
composite materials.