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High-entropy alloy and oxide descriptors and phase-prediction rules, with provenance.
High-entropy alloy and oxide descriptors and phase-prediction rules, with provenance.
Valid MCP server (1 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-dfieser-hea-bench": {
"args": [
"hea-bench-mcp",
"hea-bench"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Open, interpretable tools for computing the standard high-entropy-alloy (HEA) and high-entropy-oxide (HEO) thermodynamic and geometric descriptors and the classic empirical phase-prediction rules — from any composition, with no fitted model and no black box. Every number is a transparent closed-form expression over a curated element-property table, validated against the primary literature.
Try it now: https://dfieser.github.io/hea-bench/ — no install, runs entirely in your browser.
Using an AI coding agent to integrate this? See AGENTS.md for a machine-oriented guide to the API, exact return types and units, the fastest path to each task, and the mistakes to avoid.
For any composition it reports:
hea_bench.oxides + the apps' Oxides mode) —
rock-salt, perovskite, fluorite, and pyrochlore formability
descriptors over Shannon ionic radii with automatic charge-balance
oxidation-state assignment: per-sublattice configurational entropy,
cation size disorder, Goldschmidt t / octahedral μ / Bartel τ, the
fluorite radius-dispersion rule, and the pyrochlore radius-ratio
window.Element coverage: 37 elements for alloys (Ag Al Au Be Ca Ce Co Cr Cu Fe Gd Hf In Ir La Li Mg Mn Mo Nb Ni Os Pd Pt Re Rh Ru Sc Si Sn Ta Ti V W Y Zn Zr); the Miedema pair table covers 75; the oxide module's Shannon table covers 94.
| Surface | Where | Status |
|---|---|---|
| Python library + CLI | pip install hea-bench | done, tested |
| Zero-install browser app | https://dfieser.github.io/hea-bench/ · web/index.html | done, Python-parity-tested |
| Native desktop app | a single portable .exe, download (no install) (Tauri wrapper of the same page) | done, built from the same parity-tested core |
| MCP server for AI agents | pip install "hea-bench[mcp]", then hea-bench-mcp | done, seven tools over the same core |
The three surfaces share one calculation core. The browser/desktop
core (web/hea-calculator-core.js) is a pure-JS port of the Python
library, and tests/test_web_parity.py guarantees the two match on all
666 binary pairs and the canonical multi-element fixtures, while
tests/test_web_oxides_parity.py does the same for the oxide module,
down to identical warning messages.
pip install hea-bench
import hea_bench as hb
cantor = {"Co": 0.2, "Cr": 0.2, "Fe": 0.2, "Mn": 0.2, "Ni": 0.2}
hb.smix(cantor) # 13.381 J/(mol·K) = R · ln 5
hb.delta(cantor) # 3.164 % atomic-size mismatch
hb.vec(cantor) # 8.0 valence electrons
hb.mixing_enthalpy(cantor) # -4.16 kJ/mol (Miedema)
hb.omega(cantor) # 5.79 (Yang–Zhang)
hb.delta_chi(cantor) # 0.138 Pauling electronegativity mismatch
hb.s_excess(cantor) # 0.318 J/(mol·K) (Mansoori excess entropy)
hb.delta_g_max(cantor) # -8.00 kJ/mol (most-negative Miedema pair)
hb.phi_king(cantor) # 3.533 (King 2016 proxy)
hb.phi_ye(cantor) # 34.82 (Ye 2015 proxy)
# Apply the canonical rules
from hea_bench.rules import guo_vec, king_phi, yang_omega, ye_phi, zhang_delta
zhang_delta.predict(cantor) # 'single-phase'
yang_omega.predict(cantor) # 'single-phase'
guo_vec.predict(cantor) # 'FCC'
king_phi.predict(cantor) # 'solid_solution'
ye_phi.predict(cantor) # 'solid_solution'
These Cantor-alloy values are pinned in the test suite as the canonical sanity check. The rules are simple empirical surrogates — fast screens, not predictions; treat their output accordingly.
from hea_bench import oxides
# Rost 2015 "J14" entropy-stabilized rock salt
j14 = oxides.describe_rock_salt({"Mg": 1, "Co": 1, "Ni": 1, "Cu": 1, "Zn": 1})
j14["descriptors"]["s_config"] # 13.382 J/(mol·K) = R·ln 5
j14["oxidation_states"] # all 2+ by charge balance
# Jiang 2018 single-phase high-entropy perovskite
pvk = oxides.describe_perovskite({"Sr": 1}, {"Zr": 1, "Sn": 1, "Ti": 1, "Hf": 1, "Mn": 1})
pvk["descriptors"]["goldschmidt_t"] # 0.979, inside the 0.92–1.04 window
pvk["verdicts"]["bartel"] # 'perovskite' (τ = 3.72 < 4.18)
Each describe_* report carries the solved oxidation states, the
Shannon radii actually used, every descriptor, the formability
verdicts with their windows, and any warnings. See
examples/02_oxides_walkthrough.py
for the full tour, including the fluorite and pyrochlore screens and
oxidation-state overrides.
LLM agents hallucinate descriptor values; this server grounds them.
hea_bench.mcp_server exposes the calculator over the
Model Context Protocol as seven
deterministic tools (parse_composition, batch alloy_descriptors and
alloy_rules, omega_sensitivity, oxide_report, element_coverage,
about). Every response carries units, the citation key of each
parametrization, and the library version, so an agent's reasoning trace
contains auditable receipts rather than bare floats.
pip install "hea-bench[mcp]"
Register it with any MCP client (Claude Desktop, Cursor, ...), for
example in claude_desktop_config.json:
{ "mcpServers": { "hea-bench": { "command": "hea-bench-mcp" } } }
The omega_sensitivity tool is worth singling out: it reports the
per-pair Miedema contributions and how far Ω moves when the dominant
element's pair enthalpies are shifted within the spread of published
compilations, so an agent can ask not just for a number but for how
much to trust it.
A self-contained HTML calculator computes every descriptor, applies all six rules, runs the Miedema decompositions, and covers the oxide mode, entirely client-side. Two equivalent paths:
web/index.html — no install, no
terminal, no server.The calculator ships its own documentation: a Theory view deriving
every alloy and oxide formula with citations, a grouped, filterable
Equations reference, and a grouped References bibliography.
Deep links open a view directly (index.html#theory,
#equations, #refs). The parity-critical math lives in
web/hea-calculator-core.js and is regression-checked against Python
by the two parity test suites.
Ω = Tm·ΔSmix / |ΔHmix| diverges as ΔHmix → 0, so for
near-ideal alloys (|ΔHmix| ≲ 1–2 kJ/mol) the Ω magnitude is
extremely sensitive to the choice of Miedema pair table (sources
disagree most on Mn). The phase verdict (Ω ≫ 1.1) stays robust even when
the number does not — read Ω qualitatively in that regime.
hea-bench/
├── src/hea_bench/
│ ├── descriptors/ ΔS_mix, δ, VEC, T_m, ΔH_mix, Ω, S_E, φ + data tables
│ ├── rules/ the six empirical phase-prediction rules
│ ├── oxides/ HEO module: families, oxidation-state solver,
│ │ Shannon radii (94 elements, vendored from pymatgen)
│ ├── composition.py formula parser, normalizer
│ ├── constants.py R = 8.314
│ └── cli.py command-line entry point
├── tests/ unit tests + BOTH Python↔JS parity suites
├── web/ landing page + self-contained calculator (+ MathJax)
├── src-tauri/ native desktop wrapper (Rust/Tauri)
├── examples/ Cantor-alloy and oxides walkthroughs (.py + .ipynb)
└── pyproject.toml
git clone https://github.com/dfieser/hea-bench
cd hea-bench
pip install -e ".[dev]"
python -m pytest tests/ -q # includes the Python↔JS parity test (needs Node)
The HTML calculator (web/index.html over
web/hea-calculator-core.js) is an independent JavaScript
implementation of the same descriptors and rules. When you modify the
Python descriptor code, update the JS core to match and re-run
tests/test_web_parity.py and tests/test_web_oxides_parity.py so the
surfaces don't drift. The element data tables inside the JS core are
generated from the Python library by tests/data/_sync_js_tables.py
and tests/data/_sync_js_oxide_tables.py — regenerate, never
hand-edit.
MIT. The vendored
matminer Miedema data files remain
under their upstream BSD-3-Clause license, preserved at
descriptors/data/LICENSE.matminer.txt.
Contributions and bug reports are welcome. See
CONTRIBUTING.md for development setup and the
testing convention. To report a bug or ask a question, open a GitHub
issue; for direct contact, email the maintainer at davjfies@gmail.com.
Participation is governed by the Code of Conduct.
Citation metadata in CITATION.cff. When citing
hea-bench, please also cite the primary sources for the parametrizations
it implements: de Boer et al. 1988 for the Miedema model, the rule
papers (Yeh 2004, Zhang 2008, Guo–Liu 2011, Yang–Zhang 2012, King 2016,
Ye 2015), the oxide primaries (Shannon 1976, Goldschmidt 1926, Bartel
2019, Spiridigliozzi 2021, Subramanian 1983), matminer for the vendored
pair table, and pymatgen for the Shannon-radius digitization. The full
grouped bibliography is in the calculator's References view.
hea-bench is archived on Zenodo. The concept DOI 10.5281/zenodo.20346287 always resolves to the latest version.
Descriptor values and rule predictions reported by hea-bench are empirical estimates for research and informational purposes only. The rules and descriptors are semi-empirical surrogates with known limitations. No warranty is made as to accuracy, completeness, fitness for any particular purpose, or suitability for material qualification. Do not use these outputs as the sole basis for engineering design or material qualification without independent verification by validated thermodynamic methods (e.g. CALPHAD or DFT).
Software is provided "as is" under the MIT License.
Vendored Miedema elemental parameters from
matminer remain under
their upstream BSD-3-Clause license; see
src/hea_bench/descriptors/data/LICENSE.matminer.txt.
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