Experimental data over quantum mechanics simulations for inferring the repulsive exponent of the Lennard-Jones potential in Molecular Dynamics

Abstract

The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The potential models atomistic attraction and repulsion with century old prescribed parameters ($q=6, \; p=12$, respectively), originally related by a factor of two for simplicity of calculations. We re-examine the value of the repulsion exponent through data driven uncertainty quantification. We perform Hierarchical Bayesian inference on MD simulations of argon using experimental data of the radial distribution function (RDF) for a range of thermodynamic conditions, as well as dimer interaction energies from quantum mechanics simulations. The experimental data suggest a repulsion exponent ($p \approx 6.5$), in contrast to the quantum simulations data that support values closer to the original ($p=12$) exponent. Most notably, we find that predictions of RDF, diffusion coefficient and density of argon are more accurate and robust in producing the correct argon phase around its triple point, when using the values inferred from experimental data over those from quantum mechanics simulations. The present results suggest the need for data driven recalibration of the LJ potential across MD simulations.