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flexible-mof-gateopen | Agent execution contract

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Purpose: fast, auditable conditional equilibrium opening pressure P_eq. No web application, MD, DFT or finite-displacement phonons in the packaged route.

closed/open CIF + clean-cif reviews + gas/T + force field + pressure grid → source charges/PACMOF + RDKit/sourced guest geometry → static MACE + elastic-network entropy proxy + two RASPA isotherms → osmotic integration + PΔV → P_eq and sensitivity.

Install

Install the screening library from PyPI:

python -m pip install flexible-mof-gateopen

Core has no third-party dependencies. Install only the extras a route needs, for example flexible-mof-gateopen[structure,advanced] or flexible-mof-gateopen[ml]. Every extra is declared in pyproject.toml.

The workflow scripts and the Codex skill bundle are not shipped in the wheel. For the full workflow, clone the repository and run the commands in the next section from the checkout:

git clone https://github.com/owenxyzhang-seseTJ/flexible-mof-gateopen.git
cd flexible-mof-gateopen
python -m pip install -r requirements.txt

Execute

Run inside the skill directory with a configured Python environment:

python -m pip install -r requirements.txt
python scripts/verify_bundle.py
python scripts/self_test.py
python scripts/run_fast_screen.py --spec /absolute/job.json --output-dir /absolute/new_run

Fill assets/workflow.template.json using agent_contract.md. Paths in a job are relative to that JSON; bundled model defaults resolve from the skill root. Override raspa.binary on a different platform. Never run an unfilled template or fabricate chemistry evidence. The source repository's curated skill is under skills/flexible-mof-gateopen.

Need Script
Full workflow run_fast_screen.py
Host ΔE + entropy/free-energy proxy quick_host_free_energy.py
Clean-CIF gate check_cleaning.py
Charge assignment run_pacmof.py
Guest geometry prepare_guest_rdkit.py
GCMC input/run prepare_raspa_inputs.py
Empty-host dilute uptake audit run_empty_widom.py
Bundle/runtime checks verify_bundle.py
Analytic regressions without material simulation self_test.py

Mandatory decisions

  • Use bundled clean-cif instructions/auditor. Chemistry decisions remain reviewed; do not blindly remove disorder/solvent. Phases need identical composition and activation per formula unit; Z refers to the CIF cell, not the simulation box.
  • PACMOF RF-DDEC requires scikit-learn 1.8.0 for the bundled serialized model. Source charges require neutrality, valid atom mapping and provenance.
  • RDKit geometry does not provide adsorption LJ/charges. Custom rigid guests need explicit atom-type mapping, molecule JSON and model/geometry provenance.
  • VOCs require sourced Psat(T); never exceed it. GCMC and analysis must use the same actual fugacity. Do not extrapolate above simulated pressure.
  • A closed zero-uptake scenario is legitimate but explicit, with residual-uptake sensitivity. No accepted swaps is not exclusion; Widom is dilute evidence, not a rigorous finite-pressure upper bound.

Outputs and status

run/workflow_summary.json contains status/failures/analysis; stages retain immutable attempts, hashes and native outputs. Exit 0 means a conditional screen was produced; exit 2 means unresolved execution/scientific gates. A completed run with no opening roots means no predicted equilibrium opening within the sampled range, not at every pressure. Dense evaluation nodes do not replace GCMC.

ΔF_screen=ΔE_static+ΔU_vib,proxy−TΔS_proxy combines different models. The entropy proxy has no calibrated MOF free-energy accuracy. Scenario ranges are not confidence intervals; do not infer hysteresis, barriers or experimental accuracy. Report calibration and independent validation separately.

Included / external

Included: required project source; MACE-MOF0-v2 and PACMOF RF-DDEC weights; PACMOF source; clean-cif instructions/auditor; RASPA 3.1.0 and MACE runtime source archives; macOS arm64 RASPA executable/share tree; pinned requirements/runtime record; licenses; model provenance; manifest; JSON template; detailed theory PDF and Markdown. No development trial records, training datasets or application UI.

Python packages and OS shared libraries remain dependencies, not an offline OS distribution. The native binary may need the recorded native libraries; build the supplied source elsewhere. No web service is needed after installing dependencies. Review third-party licenses before redistribution. Only load trusted pickle/Torch files. The Theory PDF explains derivations and limitations; its reproducible XeLaTeX source is included for text/formula inspection and rebuilding.

Metadata

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