Moment tensor potential and equivariant tensor network potential with explicit dispersion interactions

Abstract

In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential, for accurate modeling of liquid carbon tetrachloride, methane, and toluene. We demonstrate that the explicit incorporation of dispersion interactions via D2 and D3 corrections significantly improves the accuracy of MLIPs when the cutoff radius is set to the commonly used value of 5–6 Å. We also show that for carbon tetrachloride and methane, a substantial improvement in accuracy can be achieved by extending the cutoff radius to 7.5 Å. However, for accurate modeling of toluene, the explicit incorporation of dispersion remains important. Furthermore, we find that MLIPs incorporating dispersion interactions via D2 reach a level of accuracy comparable to those incorporating D3, implying that D2 is suitable for accurate modeling of the systems in the study, while being less computationally expensive. We benchmarked the accuracy of the MLIPs on dimer binding curves compared to ab initio data and on predicting density and radial distribution functions compared to experiments.