pycalphad: CALPHAD-based Computational Thermodynamics in Python

Otis, Richard; Liu, Zhe

doi:10.5334/jors.140

Cited by 106 publications

(25 citation statements)

References 19 publications

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“…The second term id g mix represents the Gibbs energies, due to the ideal mixing contribution, and describes a statistic distribution of the concentrations in the phase. During the calculations with CALPHAD software packages like FactSage [36], OpenCalphad [37], Pandat [38], Pycalphad [39] and Thermo-Calc [40], this part is automatically added to the calculated Gibbs energies. The last term of Eq.…”

Section: Phase-field Modelmentioning

confidence: 99%

Data workflow to incorporate thermodynamic energies from Calphad databases into grand-potential-based phase-field models

et al. 2021

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In order to approximate Gibbs energy functions, a semi-automated framework is introduced for binary and ternary material systems, using Calphad databases. To generate Gibbs energy formulations by means of second-order polynomials, the framework includes a precise approach. Furthermore, an optional extensional step enables the modeling of systems in which a direct generation leads to the unsatisfactory results in the representation of the thermodynamics. Furthermore, an optional extensional step enables the modeling of systems, in which a direct generation leads to the unsatisfactory results, when representing the thermodynamics. Within this extension, the commonly generated functions are modified to satisfy the equilibrium conditions in the observed material systems, leading to a better correlation with thermodynamic databases. The generated Gibbs energy formulations are verified by recalculating the equilibrium concentrations of the phases and rebuilding the phase diagrams in the considered concentration and temperature ranges, prior to the simulation studies. For all comparisons, a close match is achieved between the results and the Calphad databases. As practical examples of the method, phase-field simulation studies for the directional solidification of the binary – and the ternary – eutectic systems are performed. Good agreements between the simulation results and the reported theoretical and experimental studies from literature are found, which indicates the applicability of the presented approaches. Graphical Abstract

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Section: Phase-field Modelmentioning

confidence: 99%

Data workflow to incorporate thermodynamic energies from Calphad databases into grand-potential-based phase-field models

et al. 2021

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“…In addition to thermodynamic driving force, we can also use the non-equilibrium phase diagram, predicted by the Scheil-Gulliver simulations 47,48 (see its definition in the Introduction section), to predict the formation of IMCs in fast cooling processes, such as the AM process 49,50 . Here, we used the PyCalphad software 50,87 to calculate this non-equilibrium phase diagram with the thermodynamic description modelled by Sundman et al 9 .…”

Section: Formation Of Non-equilibrium Imcs Through Thermodynamic Analysismentioning

confidence: 99%

Forming Mechanism of Equilibrium and Non-equilibrium Metallurgical Phases in Dissimilar Materials: Illustrated With Aluminum/steel (Al-Fe) Joints

Shang

Sun

Pan

et al. 2021

Preprint

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Forming metallurgical phases has a critical impact on the performance of dissimilar materials joints. Here, we shed light on the forming mechanism of equilibrium and non-equilibrium intermetallic compounds (IMCs) in the dissimilar aluminum/steel joints with respect to processing history (e.g., the pressure- and temperature-profiles) and chemical composition, where the used knowledge of free energy and atomic diffusion in the Al-Fe system was taken from first-principles phonon calculations and data available in the literature. We found that the metastable while ductile (judged by the presently predicted elastic constants) Al6Fe is a pressure (P) favored IMC observed in the processes involving high pressures. The MoSi2-type Al2Fe is a brittle and a strong P-favored IMC observed at high pressures. The stable, brittle h-Al5Fe2 is the most commonly observed IMC (followed by q-Al13Fe4) in almost all processes, such as fusion/solid-state welding and additive manufacturing (AM), since h-Al5Fe2 is temperature-favored, possessing high thermodynamic driving force of formation and the fastest atomic diffusivity among all Al-Fe IMCs. Notably the ductile AlFe3, the less ductile AlFe, and most of the other IMCs can be formed during AM, making AM a superior process to achieve desired IMCs in dissimilar materials. In addition, the unknown configurations of Al2Fe and Al5Fe2 were also examined by machine learning based datamining together with first-principles verifications and structure predictor. All the IMCs, which are not P-favored, can be identified using the conventional equilibrium phase diagram and the Scheil-Gulliver non-equilibrium simulations.

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“…The methodology discussed in the previous section is demonstrated for the Cu-Mg binary system. Bayesian inference for the CALPHAD model parameters is performed using the open source ESPEI [5] and pycalphad [19] Python packages. We assume that the reader has a working knowledge of Bayesian inference, and recommend our previous work alongside standard texts for an introduction and further detail [20], [21].…”

Section: Implementation Detailsmentioning

confidence: 99%

Quantified uncertainty in thermodynamic modeling for materials design

et al. 2019

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Phase fractions, compositions and energies of the stable phases as a function of macroscopic composition, temperature, and pressure (X-T-P) are the principle correlations needed for the design of new materials and improvement of existing materials. They are the outcomes of thermodynamic modeling based on the CALculation of PHAse Diagrams (CALPHAD) approach. The accuracy of CALPHAD predictions vary widely in X-T-P space due to experimental error, model inadequacy and unequal data coverage. In response, researchers have developed frameworks to quantify the uncertainty of thermodynamic property model parameters and propagate it to phase diagram predictions. In most previous studies, uncertainty was represented as intervals on phase boundaries (with respect to composition or temperature) or invariant reactions (with respect to temperature) and was unable to represent the uncertainty in eutectoid invariant reactions or in the stability of phase regions. In this work, we propose a suite of tools that leverages samples from the multivariate model parameter distribution to represent uncertainty in forms that surpass previous limitations and are well suited to materials design. These representations include the distribution of phase diagrams and their features, as well as the dependence of phase stability and the distributions of phase fraction, composition, activity and Gibbs energy on X-T-P location -irrespective of the total number of components. Most critically, the new methodology allows the material designer to interrogate a certain composition and temperature domain and get in return the probability of different phases to be stable, which can positively impact materials design.

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pycalphad: CALPHAD-based Computational Thermodynamics in Python

Cited by 106 publications

References 19 publications

Data workflow to incorporate thermodynamic energies from Calphad databases into grand-potential-based phase-field models

Data workflow to incorporate thermodynamic energies from Calphad databases into grand-potential-based phase-field models

Forming Mechanism of Equilibrium and Non-equilibrium Metallurgical Phases in Dissimilar Materials: Illustrated With Aluminum/steel (Al-Fe) Joints

Quantified uncertainty in thermodynamic modeling for materials design

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