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Updated: Jul 24, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
Published on: September 17, 2021
Prediction of ionic conductivity from adiabatic heating in non-equilibrium molecular dynamics on various test systems
Philippe Ungerer1, Benoit Minisini2
1Materials Design SA, 42 Av. Verdier, 92120, Montrouge, France.
Context:
The evaluation of ionic conductivity through atomistic modeling typically involves calculating diffusion coefficients, which often necessitates simulations spanning several hundreds of nanoseconds. This study introduces a less computationally demanding approach based on non-equilibrium molecular dynamics applicable to a wide range of systems.
Method:
Ionic conductivity is determined by evaluating the Joule heating effect recorded during non-equilibrium molecular dynamics (NEMD) simulations. These simulations which involve applying a uniform electric field using classical force fields in LAMMPS are conducted within the MedeA software environment. The conductivity value for a specific temperature can thus be obtained from a single simulation together with an estimation of the associated uncertainty. Guidelines for selecting NEMD parameters such as electric field intensity and initial temperature are proposed to satisfy linear irreversible transport.
Results:
The protocol presented in this study is applied to four different types of systems, namely, (i) molten NaCl, (ii) NaCl and LiCl aqueous solutions, (iii) solution of ionic liquid with two solvents, and (iv) NaX zeolites in the anhydrous and hydrated states. The main advantages of the proposed protocol are simplicity of implementation (eliminating the need to store individual ion trajectories), reliability (low electric field, linear response, no perturbation of the equations of motion by a thermostat), and a wide range of applications. The estimated contribution of field-induced drift motion of ions to kinetic energy appears very low, justifying the use of standard kinetic energy in the method. For each system, the reported influence of temperature, ion concentration, solvent nature, or hydration is correctly predicted.
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