Dataset Information

New Insight into the Ground State of FePc: A Diffusion Monte Carlo Study.

ABSTRACT: We have applied DMC to evaluate relative stability of the possible electronic configurations of an isolated FePc under D 4h symmetry, considering some fixed nodes generated from different methods. They predict A 2g ground state consistently, supporting preceding DFT studies, with confidence overcoming the ambiguity about exchange-correlation (XC) functionals. By comparing DMC with several XC, we clarified the importance of the short-range exchange to describe the relative stability. We examined why the predicted A 2g is excluded from possible ground states in the recent ligand field based model. Simplified assumptions made in the superposition model are identified to give unreasonably less energy gain for A 2g when compared with the reality. The state is found to have possible reasons for the stabilization, reducing the occupations from an unstable anti-bonding orbital, avoiding double occupation of a spatially localized orbital, and gaining exchange energy by putting a triplet spin pair in degenerate orbitals.

SUBMITTER: Ichibha T

PROVIDER: S-EPMC5435675 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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New Insight into the Ground State of FePc: A Diffusion Monte Carlo Study.

Ichibha Tom T Hou Zhufeng Z Hongo Kenta K Maezono Ryo R

Scientific reports 20170517 1

We have applied DMC to evaluate relative stability of the possible electronic configurations of an isolated FePc under D <sub>4h</sub> symmetry, considering some fixed nodes generated from different methods. They predict A <sub>2g</sub> ground state consistently, supporting preceding DFT studies, with confidence overcoming the ambiguity about exchange-correlation (XC) functionals. By comparing DMC with several XC, we clarified the importance of the short-range exchange to describe the relative s ...[more]

PMID: 28515425

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Project description:In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.

Dataset Information

New Insight into the Ground State of FePc: A Diffusion Monte Carlo Study.

Publications

New Insight into the Ground State of FePc: A Diffusion Monte Carlo Study.

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