Statistical approach for automated weighting of datasets: Application to heat capacity data

Zomorodpoosh, Setareh; Bocklund, Brandon; Obaied, Abdulmonem; Otis, Richard; Liu, Zhe; Roslyakova, Irina

doi:10.1016/j.calphad.2020.101994

Cited by 9 publications

(4 citation statements)

References 55 publications

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“…This software will be available at ICAMS homepage [21]. In the following, first general software features will be presented and then the modified cross-validation approach [22,23] and the parameter selection method are described in detail.…”

Section: Automatisationmentioning

confidence: 99%

“…It can be directly seen, that data-set 10 differs significantly from the other ones. The modified K-fold cross-validation (KFCV) method, which is proposed as a reproducible method to weight heat capacity data-sets automatically [22,23] can be adapted and applied to tracer diffusion data. In original KFCV all data are split into k equal-sized data-sets (typically k = 5-10) randomly.…”

Section: Cross-validationmentioning

confidence: 99%

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Automated assessment of a kinetic database for fcc Co–Cr–Fe–Mn–Ni high entropy alloys

Abrahams

Zomorodpoosh

khorasgani

et al. 2021

Modelling Simul. Mater. Sci. Eng.

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The development of accurate kinetic databases by parametrizing the composition and temperature dependence of elemental atomic mobilities, is essential for correct multicomponent calculations and simulations. In this work the automated assessment procedure for the establishment of CALPHAD-type kinetic databases is proposed, including the storage of raw data and assessment results, automatic weighting of data, parameter selection and automated reassessments. This allows the establishment of reproducible up-to-date databases. The proposed software, written in python, is applied to the assessment of a kinetic database for the fcc Co–Cr–Fe–Mn–Ni high entropy alloy using only tracer diffusion data for a sharp separation of thermodynamic and kinetic data. The established database is valid for the whole composition range of the five-component high entropy alloy.

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Section: Automatisationmentioning

confidence: 99%

Section: Cross-validationmentioning

confidence: 99%

Automated assessment of a kinetic database for fcc Co–Cr–Fe–Mn–Ni high entropy alloys

Abrahams

Zomorodpoosh

khorasgani

et al. 2021

Modelling Simul. Mater. Sci. Eng.

Self Cite

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show abstract

“…The further development of the approach [6] includes uncertainty estimation of predicted results based on uncertainty in model parameters via Monte Carlo Markov chains combined with the Bayesian inference [7,8]. Another problem related to a mixture of experimental and calculation results was approached recently in [9] with k-fold crossvalidation of the input datasets.…”

Section: Introductionmentioning

confidence: 99%

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

et al. 2021

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Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free-energy functions using data of various origins. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on a Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with the existing studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.

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Section: Introductionmentioning

confidence: 99%

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

Ladygin,

Beniya,

Makarov

et al. 2021

Preprint

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Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free energy functions using data of various origin. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with existing works studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.

show abstract

Statistical approach for automated weighting of datasets: Application to heat capacity data

Cited by 9 publications

References 55 publications

Automated assessment of a kinetic database for fcc Co–Cr–Fe–Mn–Ni high entropy alloys

Automated assessment of a kinetic database for fcc Co–Cr–Fe–Mn–Ni high entropy alloys

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

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