scholarly journals Diffusion Monte Carlo evaluation of disiloxane linearisation barrier

2021 ◽  
Author(s):  
Adie Tri Hanindriyo ◽  
Amit Kumar Singh Yadav ◽  
Tom Ichibha ◽  
Ryo Maezono ◽  
Kosuke Nakano ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Quantum Chemistry ◽  
Diffusion Monte Carlo ◽  
Significant Interest ◽  
Monte Carlo Evaluation

The disiloxane molecule is a prime example of silicate compounds containing the Si-O-Si bridge. The molecule is of significant interest within the field of quantum chemistry, owing to the difficulty...

scholarly journals Nonlocal pseudopotentials and time-step errors in diffusion Monte Carlo

2021 ◽  
Vol 154 (21) ◽  
pp. 214110
Author(s):  
Tyler A. Anderson ◽  
C. J. Umrigar
Keyword(s):  
Monte Carlo ◽  
Diffusion Monte Carlo ◽  
Time Step

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

Quantifying electron-correlation effects in small coinage-metal clusters by ab initio calculations

2021 ◽  
Author(s):  
Victor Giovanni de Pina ◽  
Bráulio Gabriel Alencar Brito ◽  
Guo -Q Hai ◽  
Ladir Cândido
Keyword(s):  
Monte Carlo ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Electron Correlation ◽  
Metal Clusters ◽  
Correlation Effects ◽  
Diffusion Monte Carlo ◽  
Coinage Metal ◽  
Electron Correlation Effects ◽  
Fixed Node

We investigate many-electron correlation effects in neutral and charged coinage-metal clusters Cun, Agn, and Aun (n = 1 − 4) by ab initio calculations using fixed-node diffusion Monte Carlo (FN-DMC)...

scholarly journals Toward a systematic improvement of the fixed-node approximation in diffusion Monte Carlo for solids—A case study in diamond

2020 ◽  
Vol 153 (18) ◽  
pp. 184111
Author(s):  
Anouar Benali ◽  
Kevin Gasperich ◽  
Kenneth D. Jordan ◽  
Thomas Applencourt ◽  
Ye Luo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Diffusion Monte Carlo ◽  
Systematic Improvement ◽  
Fixed Node

scholarly journals Quadratic diffusion Monte Carlo and pure estimators for atoms

2002 ◽  
Vol 116 (14) ◽  
pp. 5956-5962 ◽  
Author(s):  
A. Sarsa ◽  
J. Boronat ◽  
J. Casulleras
Keyword(s):  
Monte Carlo ◽  
Diffusion Monte Carlo
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