Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

作     者：Pedram Tavadze Reese Boucher Guillermo Avendaño-Franco Keenan X.Kocan Sobhit Singh Viviana Dovale-Farelo Wilfredo Ibarra-Hernández Matthew B.Johnson David S.Mebane Aldo H.Romero 

作者机构：Department of Physics and AstronomyWest Virginia UniversityMorgantownWVUSA Department of Mechanical and Aerospace EngineeringWest Virginia UniversityMorgantownWVUSA Department of Physics and AstronomyRutgers UniversityPiscatawayNJUSA Facultad de IngenieríaBenemérita Universidad Autónoma de PueblaApdo.Postal J-39PueblaPue.72570México 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：1661-1669页

核心收录：

学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 070101[理学-基础数学] 

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 

主　　题：materials Hubbard parameter 

摘      要：The density-functional theory is widely used to predict the physical properties of ***,it usually fails for strongly correlated materials.A popular solution is to use the Hubbard correction to treat strongly correlated electronic ***,the values of the Hubbard U and J parameters are initially unknown,and they can vary from one material to *** 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 *** has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based ***,PBE predicts lattice parameters reasonably well without the Hubbard correction.