Understanding the difference in cohesive energies between alpha and beta tin in DFT calculations
arXiv:1601.05528 · doi:10.1063/1.4948434
Abstract
The transition temperature between the low-temperature alpha phase of tin to beta tin is close to the room temperature (Tab =13C), and the difference in cohesive energy of the two phases at 0 K of about dEcoh=0.02 eV/atom is at the limit of the accuracy of DFT (density functional theory) with available exchange-correlation functionals. It is however critically important to model the relative phase energies correctly for any reasonable description of phenomena and technologies involving these phases, for example, the performance of tin electrodes in electrochemical batteries. Here, we show that several commonly used and converged DFT setups using the most practical and widely used PBE functional result in dEcoh of about 0.04 eV/atom, with different types of basis sets and with different models of core electrons (all-electron or pseudopotentials of different types), which leads to a significant overestimation of Tab. We show that this is due to the errors in relative positions of s and p -like bands, which, combined with different populations of these bands in alpha and beta Sn, leads to overstabilization of alpha tin. We show that this error can be effectively corrected by applying a Hubbard +U correction to s -like states, whereby correct cohesive energies of both alpha and beta Sn can be obtained with the same computational scheme. We quantify for the first time the effects of anharmonicity on dEcoh and find that it is negligible.
7 pages, 5 figures
References in corpus (2)
Cited by in corpus (8)
- Data-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression
- Material Size Dependence on Fundamental Constants
- Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials
- Non-parametric Local Pseudopotentials with Machine Learning: a Tin Pseudopotential Built Using Gaussian Process Regression
- Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag
- The Tin Pest Problem as a Test of Density Functionals Using High-Throughput Calculations
- Developing Potential Energy Surfaces for Graphene-based 2D-3D Interfaces from Modified High Dimensional Neural Networks for Applications in Energy Storage
- Synchrotron x-ray diffraction studies of the structural phase transition in Sn and Sn-Cu