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arxiv: 2606.19661 · v1 · pith:4SRXP2AInew · submitted 2026-06-18 · ❄️ cond-mat.mtrl-sci

HEACalculator: An Open-Source Python Tool for Thermodynamic Property Calculation and Solid Solution Prediction in High-Entropy Alloys

Do\u{g}uhan Sar{\i}t\"urk , Yunus Eren Kalay , Raymundo Arr\'oyave This is my paper

Pith reviewed 2026-06-26 16:52 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci
keywords high-entropy alloyssolid solution predictionthermodynamic descriptorsPython packagemixing enthalpyconfigurational entropyHEA designphase stability
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The pith

HEACalculator computes sixteen thermodynamic descriptors and evaluates eight published solid-solution rules for high-entropy alloys from one curated dataset.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents an open-source Python package that brings together calculations of mixing enthalpy, configurational entropy, valence electron concentration, atomic size mismatch, and related stability parameters in a single workflow. It also applies eight existing prediction criteria to assess whether a multi-principal-element composition is likely to remain a single-phase solid solution. A sympathetic reader would care because these checks occur early in alloy design and currently require piecing together separate data sources and formulas. The package supplies the calculations through command-line, graphical, and programming interfaces so that screening can be done consistently and quickly before more expensive thermodynamic modeling or experiments begin.

Core claim

HEACalculator is an open-source Python package that computes sixteen commonly used thermodynamic and structural quantities, including mixing enthalpy, configurational entropy, valence electron concentration, Hume-Rothery electron-to-atom ratio, atomic size mismatch, electronegativity mismatch, and derived parameters such as Omega, Lambda, and Phi, while simultaneously evaluating eight published solid-solution formation criteria against a single curated elemental and binary-interaction dataset. The package is offered in three access modes: command-line interface, desktop graphical user interface, and Python API.

What carries the argument

The HEACalculator package, which combines a curated elemental and binary-interaction dataset with CLI, GUI, and API access modes to compute the sixteen descriptors and apply the eight prediction criteria in one place.

If this is right

  • Users can screen candidate compositions for single-phase likelihood using a uniform set of descriptors without assembling separate data tables or scripts.
  • The same composition can be evaluated against all eight published rules at once, revealing where the rules agree or disagree.
  • Integration into notebooks or larger screening pipelines becomes possible through the provided Python API.
  • Early-stage alloy design workflows can insert the thermodynamic checks before committing resources to CALPHAD modeling or synthesis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The package could serve as a reference implementation that makes it easier to test new prediction rules against the same baseline calculations.
  • If the underlying dataset is extended with additional binary systems, the same code base could support screening in higher-order alloys without rewriting the descriptor formulas.
  • Routine use might highlight which of the eight criteria are most or least consistent with experimental outcomes, prompting refinement of those rules.

Load-bearing premise

The curated elemental and binary-interaction dataset accurately reflects the interactions required for reliable multi-component calculations, and the eight published prediction criteria function as appropriate screening tools.

What would settle it

Direct comparison of the package outputs for a set of experimentally verified single-phase and multi-phase high-entropy alloys against independent thermodynamic databases or experimental phase diagrams would show systematic mismatches in the computed descriptors or incorrect predictions from the eight criteria.

Figures

Figures reproduced from arXiv: 2606.19661 by Do\u{g}uhan Sar{\i}t\"urk, Raymundo Arr\'oyave, Yunus Eren Kalay.

Figure 1
Figure 1. Figure 1: The HEACalculator graphical interface showing the HEA Parameters page. Users select elements from the periodic table, assign atomic-percent compositions, and calculate thermodynamic descriptors and solid-solution predictions in a single shared results view. Software Design HEACalculator is organized around a shared calculation core rather than around its user interfaces. Compositions supplied through the C… view at source ↗
read the original abstract

High-entropy alloys (HEAs) have attracted sustained interest since their introduction by Cantor et al. and Yeh et al. because multi-principal-element compositions can exhibit unusual combinations of strength, thermal stability, and functional performance. A recurring problem in HEA design is determining whether a candidate composition is likely to form a single-phase solid solution or instead separate into multiple phases or intermetallic compounds. That question sits early in the alloy-design workflow because it shapes which compositions require further thermodynamic analysis, synthesis, and experimental validation. HEACalculator is an open-source Python package for calculating thermodynamic and structural descriptors used in HEA research and for evaluating published solid-solution formation rules in a single place. It computes sixteen commonly used quantities, including mixing enthalpy, configurational entropy, valence electron concentration, Hume-Rothery electron-to-atom ratio, atomic size mismatch, electronegativity mismatch, and derived stability parameters such as Omega, Lambda, and Phi, and it evaluates eight published prediction criteria. The package combines a curated elemental and binary-interaction dataset with three access modes: a command-line interface (CLI), a desktop graphical user interface (GUI), and a Python application programming interface (API) for programmatic use in notebooks and screening workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript presents HEACalculator, an open-source Python package that computes sixteen thermodynamic and structural descriptors commonly used in high-entropy alloy (HEA) research—including mixing enthalpy, configurational entropy, valence electron concentration, Hume-Rothery e/a ratio, atomic size mismatch, electronegativity mismatch, and derived parameters such as Omega, Lambda, and Phi—and evaluates eight published solid-solution formation criteria. The package integrates a curated elemental and binary-interaction dataset and provides three access modes: CLI, GUI, and Python API.

Significance. If the dataset provenance and numerical implementations prove reliable, the tool would consolidate multiple published HEA screening methods into a single, accessible open-source resource with multiple interfaces, potentially aiding reproducibility and workflow integration for composition screening. The open-source release and provision of CLI/GUI/API modes are concrete strengths that lower barriers to use.

major comments (2)
  1. [Abstract] Abstract: the central claim that the package enables reliable solid-solution prediction for multi-component HEAs rests on the accuracy of the curated elemental and binary-interaction dataset for extrapolation beyond binaries, yet the manuscript supplies no provenance details, no comparison to independent sources (Miedema, CALPHAD), and no benchmark against measured ternary or quaternary enthalpies.
  2. [Abstract] The description of the dataset (Abstract) contains no error-propagation analysis or validation of the sixteen computed quantities against independent calculations or experiments, which is load-bearing for any claim that the outputs are usable for HEA screening.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript describing HEACalculator. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the package enables reliable solid-solution prediction for multi-component HEAs rests on the accuracy of the curated elemental and binary-interaction dataset for extrapolation beyond binaries, yet the manuscript supplies no provenance details, no comparison to independent sources (Miedema, CALPHAD), and no benchmark against measured ternary or quaternary enthalpies.

    Authors: We agree that explicit documentation of dataset provenance is absent from the current manuscript. The elemental and binary parameters are drawn from standard literature sources routinely cited in HEA studies (Miedema model and related compilations), but these origins are not detailed in the text. The manuscript's scope is the implementation and integration of existing descriptors rather than a new validation campaign; therefore no direct comparisons to CALPHAD or new ternary/quaternary benchmarks are provided. In the revised version we will add a dedicated 'Dataset Sources' subsection citing the primary references and will insert a limitations paragraph that explicitly notes the extrapolation assumptions when applying binary-derived parameters to multi-component systems. revision: yes

  2. Referee: [Abstract] The description of the dataset (Abstract) contains no error-propagation analysis or validation of the sixteen computed quantities against independent calculations or experiments, which is load-bearing for any claim that the outputs are usable for HEA screening.

    Authors: The sixteen descriptors are evaluated from deterministic algebraic expressions taken directly from the cited literature; consequently no statistical error-propagation analysis applies. The package reproduces published formulas rather than generating new experimental data, so independent experimental validation is outside the stated contribution. We will revise the abstract to clarify that the tool implements established methods and will add a short 'Limitations and Scope' paragraph discussing the deterministic character of the calculations and the absence of new experimental benchmarks. These changes will better frame the reliability context for users without expanding the paper beyond a software-tool description. revision: yes

Circularity Check

0 steps flagged

No circularity: tool implements external published criteria from curated dataset

full rationale

The manuscript describes a software package that computes 16 standard descriptors (mixing enthalpy, Omega, atomic size mismatch, etc.) and evaluates 8 published solid-solution criteria drawn from prior literature. These quantities are obtained from a curated elemental/binary dataset whose values are inputs, not outputs fitted inside the paper. No equations, predictions, or uniqueness claims are presented that reduce by construction to parameters defined or optimized within this work; the contribution is integration and accessibility rather than a derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract describes a software implementation of existing published rules and a curated dataset; no new free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.1-grok · 5775 in / 1128 out tokens · 34294 ms · 2026-06-26T16:52:11.228202+00:00 · methodology

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Reference graph

Works this paper leans on

21 extracted references · 4 canonical work pages

  1. [1]

    and Chang, I

    Cantor, B. and Chang, I. T. H. and Knight, P. and Vincent, A. J. B. , title =. Materials Science and Engineering: A , year =

  2. [2]

    and Chen, S.-K

    Yeh, J.-W. and Chen, S.-K. and Lin, S.-J. and Gan, J.-Y. and Chin, T.-S. and Shun, T.-T. and Tsau, C.-H. and Chang, S.-Y. , title =. Advanced Engineering Materials , year =

  3. [3]

    George, E. P. and Raabe, D. and Ritchie, R. O. , title =. Nature Reviews Materials , year =

  4. [4]

    Miracle, D. B. and Senkov, O. N. , title =. Acta Materialia , year =

  5. [5]

    and Zhang, Y

    Yang, X. and Zhang, Y. , title =. Materials Chemistry and Physics , year =

  6. [6]

    and Ng, C

    Guo, S. and Ng, C. and Lu, J. and Liu, C. T. , title =. Journal of Applied Physics , year =

  7. [7]

    and Huang, Y

    Wang, Z. and Huang, Y. and Yang, Y. and Wang, J. and Liu, C. T. , title =. Scripta Materialia , year =

  8. [8]

    Singh, A. K. and Kumar, N. and Dwivedi, A. and Subramaniam, A. , title =. Intermetallics , year =

  9. [9]

    Ye, Y. F. and Wang, Q. and Lu, J. and Liu, C. T. and Yang, Y. , title =. Scripta Materialia , year =

  10. [10]

    Troparevsky, M. C. and Morris, J. R. and Kent, P. R. C. and Lupini, A. R. and Stocks, G. M. , title =. Physical Review X , year =

  11. [11]

    Senkov, O. N. and Miracle, D. B. , title =. Journal of Alloys and Compounds , year =

  12. [12]

    King, D. J. M. and Middleburgh, S. C. and McGregor, A. G. and Cortie, M. B. , title =. Acta Materialia , year =

  13. [13]

    and Inoue, A

    Takeuchi, A. and Inoue, A. , title =. Materials Transactions , year =

  14. [14]

    and Madrid-Cortes, C

    Martin, P. and Madrid-Cortes, C. E. and C. Computer Physics Communications , year =. doi:10.1016/j.cpc.2022.108398 , url =

    work page doi:10.1016/j.cpc.2022.108398 2022

  15. [15]

    and Dunn, A

    Ward, L. and Dunn, A. and Faghaninia, A. and Zimmermann, N. E. R. and Bajaj, S. and Wang, Q. and Montoya, J. H. and Chen, J. and Bystrom, K. and Dylla, M. and Chard, K. and Asta, M. and Persson, K. A. and Snyder, G. J. and Foster, I. and Jain, A. , title =. Computational Materials Science , year =

  16. [16]

    and Kunwar, A

    Subedi, U. and Kunwar, A. and Coutinho, Y. A. and Gyanwali, K. , title =. Metals and Materials International , year =

  17. [17]

    and Coutinho, Y

    Subedi, U. and Coutinho, Y. A. and Malla, P. B. and Gyanwali, K. and Kunwar, A. , title =. Metals , year =

  18. [18]

    2019 , publisher =

    Sarıtürk, Doğuhan , title =. 2019 , publisher =. doi:10.5281/zenodo.3590318 , url =

    work page doi:10.5281/zenodo.3590318 2019

  19. [19]

    and Qian, Xiaoning and Karaman, Ibrahim and Arroyave, Raymundo , title =

    Zadeh, Sina Hossein and Cakirhan, Cem and Khatamsaz, Danial and Broucek, John and Brown, Timothy D. and Qian, Xiaoning and Karaman, Ibrahim and Arroyave, Raymundo , title =. Materials and Design , year =. doi:10.1016/j.matdes.2024.113096 , url =

    work page doi:10.1016/j.matdes.2024.113096 2024

  20. [20]

    Materials Letters , year =

    Sheikh, Sofia and Zadeh, Sina Hossein and Mulukutla, Mrinalini and Hastings, Trevor and Arroyave, Raymundo , title =. Materials Letters , year =. doi:10.1016/j.matlet.2025.138940 , url =

    work page doi:10.1016/j.matlet.2025.138940 2025

  21. [21]

    and Hu, Q

    Guo, S. and Hu, Q. and Ng, C. and Liu, C. T. , title =. Intermetallics , year =