Nested Markov chain Monte Carlo sampling of a density functional theory potential: Equilibrium thermodynamics of dense fluid nitrogen

2009 ◽  
Vol 131 (7) ◽  
pp. 074105 ◽  
Author(s):  
Joshua D. Coe ◽  
Thomas D. Sewell ◽  
M. Sam Shaw
Keyword(s):  
Density Functional Theory ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Density Functional ◽  
Monte Carlo Sampling ◽  
Equilibrium Thermodynamics ◽  
Functional Theory ◽  
Dense Fluid ◽  
Potential Equilibrium

Monte-Carlo simulation combined with density functional theory to investigate the equilibrium thermodynamics of electrode materials: lithium titanates as model compounds

2019 ◽  
Vol 21 (28) ◽  
pp. 15551-15559 ◽  
Author(s):  
Hiroyuki Ozaki ◽  
Kohei Tada ◽  
Tetsu Kiyobayashi
Keyword(s):  
Density Functional Theory ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Electrode Material ◽  
Density Functional ◽  
Electrode Materials ◽  
Equilibrium Thermodynamics ◽  
Model Compounds ◽  
Functional Theory ◽  
The Density Functional Theory

The present study proposes a method to describe the equilibrium thermodynamics of a model electrode material, LTO, based on the Monte-Carlo simulation (MC), for which the energetic parameters are determined by the density functional theory (DFT).

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

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pedram Tavadze ◽  
Reese Boucher ◽  
Guillermo Avendaño-Franco ◽  
Keenan X. Kocan ◽  
Sobhit Singh ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Physical Properties ◽  
Parameter Space ◽  
Density Functional ◽  
Monte Carlo Sampling ◽  
Strongly Correlated ◽  
Bayesian Calibration ◽  
Iron Based

AbstractThe 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.

scholarly journals Microstructure of Flow-Driven Suspension of Hardspheres in Cylindrical Confinement: A Dynamical Density Functional Theory and Monte Carlo Study

Langmuir ◽  
2017 ◽  
Vol 33 (42) ◽  
pp. 11332-11344 ◽  
Author(s):  
Hsiu-Yu Yu ◽  
Zahera Jabeen ◽  
David M. Eckmann ◽  
Portonovo S. Ayyaswamy ◽  
Ravi Radhakrishnan
Keyword(s):  
Density Functional Theory ◽  
Monte Carlo ◽  
Density Functional ◽  
Monte Carlo Study ◽  
Functional Theory
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