摘要
The density-functional theory is widely used to predict the physical properties of materials.However,it usually fails for strongly correlated materials.A popular solution is to use the Hubbard correction to treat strongly correlated electronic states.Unfortunately,the values of the Hubbard U and J parameters are initially unknown,and they can vary from one material to another.In this semi-empirical study,we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties(volume,magnetic moment,and bandgap).We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals(LDA,PBE,and PBEsol).We found that LDA requires the largest U correction.PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds.Lastly,PBE predicts lattice parameters reasonably well without the Hubbard correction.
基金
This work used the XSEDE which is supported by the National Science Foundation(NSF)(ACI-1053575)
The authors also acknowledge the support from the Texas Advanced Computing Center and the Pittsburgh Supercomputing Center(with the Stampede2 and Bridges supercomputers).We also acknowledge the use of the Thorny Flat Cluster at WVU,which is funded in part by the NSF Major Research Instrumentation Program(MRI)Award(MRI-1726534)
Additionally,we acknowledge the support of O’Brien Fund of the WVU Energy Institute and the Summer Undergraduate Research Experience(SURE)at WVU.The research effort on the code development and the electronic structure calculations from A.H.R.,P.T.,and R.B.in this project has been supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences under Award Number DE-SC0021375
Figures in this paper were generated using the Matplotlib110 and PyVista111 Python packages.We used Numpy112 and SciPy113 Python packages for preand post-processing of the results.
