  Machine learning force fields (MLFFs) have emerged as a promising approach to
bridge the accuracy of quantum mechanical methods and the efficiency of
classical force fields. However, the abundance of MLFF models and the challenge
of accurately predicting atomic forces pose significant obstacles in their
practical application. In this paper, we propose a novel ensemble learning
framework, EL-MLFFs, which leverages the stacking method to integrate
predictions from diverse MLFFs and enhance force prediction accuracy. By
constructing a graph representation of molecular structures and employing a
graph neural network (GNN) as the meta-model, EL-MLFFs effectively captures
atomic interactions and refines force predictions. We evaluate our approach on
two distinct datasets: methane molecules and methanol adsorbed on a Cu(100)
surface. The results demonstrate that EL-MLFFs significantly improves force
prediction accuracy compared to individual MLFFs, with the ensemble of all
eight models yielding the best performance. Moreover, our ablation study
highlights the crucial roles of the residual network and graph attention layers
in the model's architecture. The EL-MLFFs framework offers a promising solution
to the challenges of model selection and force prediction accuracy in MLFFs,
paving the way for more reliable and efficient molecular simulations.
