ZHU YINGHAO

Author(s): A. E. Taylor, R. Morrow, M. D. Lumsden, S. Calder, M. H. Upton, A. I. Kolesnikov, M. B. Stone, R. S. Fishman, A. Paramekanti, P. M. Woodward, and A. D. Christianson

Sr2FeOsO6 is an insulating double perovskite compound which undergoes antiferromagnetic transitions at 140 (TN1) and 67 K (TN2). To study the underlying electronic and magnetic interactions giving rise to this behavior we have performed inelastic neutron scattering (INS) and resonant inelastic x-ray...

[Phys. Rev. B 98, 214422] Published Wed Dec 12, 2018

Applied Physics Reviews, Volume 5, Issue 3, September 2018.
Molecular simulation is a powerful computational tool for a broad range of applications including the examination of materials properties and accelerating drug discovery. At the heart of molecular simulation is the analytic potential energy function. These functions span the range of complexity from very simple functions used to model generic phenomena to complex functions designed to model chemical reactions. The complexity of the mathematical function impacts the computational speed and is typically linked to the accuracy of the results obtained from simulations that utilize the function. One approach to improving accuracy is to simply add more parameters and additional complexity to the analytic function. This approach is typically used in non-reactive force fields where the functional form is not derived from quantum mechanical principles. The form of other types of potentials, such as the bond-order potentials, is based on quantum mechanics and has led to varying levels of accuracy and transferability. When selecting a potential energy function for use in molecular simulations, the accuracy, transferability, and computational speed must all be considered. In this focused review, some of the more commonly used potential energy functions for molecular simulations are reviewed with an eye toward presenting their general forms, strengths, and weaknesses.