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1篇 您的检索式：作者名="David S.Mebane"

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		题名	作者	年代	出处	被引量
	1	Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling显示文摘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.	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	2021	npj Computational Materials2021，，1：	0

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