Abstract
Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events. As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics. In this paper, we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors. In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation. Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion. We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties. Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.
Highlights
ab initio molecular dynamics (AIMD) simulations are carried out using the potential energy surface and atomistic forces calculated from ab initio methods, such as density functional theory (DFT), and model the dynamics of atomistic systems with ab initio level of accuracy and chemical versatility
This accuracy is lacking in classical molecular dynamics (MD) simulations, which are based on interatomic potentials (a.k.a. force fields)
AIMD simulation is the method of choice for studying the dynamics of atoms with complex chemistry changes and simulating materials that cannot be described by available interatomic potentials
Summary
As a powerful modeling technique, ab initio molecular dynamics (AIMD) simulation has been recently applied to a wide range of research topics in chemistry and materials science.[1,2,3,4] AIMD simulations are carried out using the potential energy surface and atomistic forces calculated from ab initio methods, such as density functional theory (DFT), and model the dynamics of atomistic systems with ab initio level of accuracy and chemical versatility. This accuracy is lacking in classical molecular dynamics (MD) simulations, which are based on interatomic potentials (a.k.a. force fields). The wide applicability of AIMD simulations has been successfully demonstrated in studies of diffusional properties,[5,6,7,8,9] reaction processes,[10,11] vibrational frequency,[12,13,14] amorphous materials,[15,16,17] phase transition,[18] etc
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
