mbe-tools 0.1.1

A practical toolkit for Many-Body Expansion (MBE) workflows: cluster design, MBE input generation, output parsing, and analysis.

mbe-tools

mbe-tools is a Python package that covers the full Many-Body Expansion (MBE) loop:

• Cluster design: read .xyz, extract fragments, and sample subsets (with optional ion retention).
• MBE job prep: generate subset geometries, build Q-Chem/ORCA inputs, and emit PBS/Slurm job scripts (with optional chunked submission).
• Parsing: read ORCA / Q-Chem outputs, infer method/basis/grid metadata from paths or companion inputs, and write JSONL.
• Analysis: inclusion–exclusion MBE(k), summaries, CSV/Excel export, and basic plots.

Status: 0.1.0 (MVP). Backend syntax (e.g., ghost atoms) may need local tweaks.

What changed (P0–P5)

• P0: Settings loader and precedence (mbe.toml, env vars) for commands/modules/scratch/queue hints.
• P1: Parser auto-detects program and infers method/basis/grid metadata from paths; emits detection + error tags.
• P2: Spatial fragment sampling mode with centroid-based compact selection and ion retention.
• P3: Connectivity-driven fragmentation and fragment labeling (water/methanol/ethanol/benzene/ions).
• P4: Tunable MBE params and input builders (thresh/tole/scf, grid) exposed in CLI.
• P5: Run-control for PBS/Slurm templates (regex confirmation, retries/cleanup, optional delete, state JSON).

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Install (editable for development)

cd mbe-tools
python -m pip install -e .[analysis,cli]

Global settings (P0)

Configure default commands/modules/scratch once and reuse across CLI calls. Precedence (lowest → highest): environment variables → ~/.config/mbe-tools/config.toml → ./mbe.toml → explicit path passed to load_settings(path=...).

Supported keys: qchem_command, orca_command, qchem_module, orca_module, scratch_dir, scheduler_queue, scheduler_partition, scheduler_account.

Environment variable map: MBE_QCHEM_CMD, MBE_ORCA_CMD, MBE_QCHEM_MODULE, MBE_ORCA_MODULE, MBE_SCRATCH, MBE_SCHED_QUEUE, MBE_SCHED_PARTITION, MBE_SCHED_ACCOUNT.

Minimal mbe.toml example (edit paths/modules to your site):

qchem_command = "/opt/qchem/bin/qchem"
orca_command  = "/opt/orca/bin/orca"
qchem_module  = "qchem/5.2.2"
orca_module   = "orca/5.0.3"
scratch_dir   = "/scratch/${USER}"
scheduler_queue = "normal"          # PBS
scheduler_partition = "work"        # Slurm
scheduler_account = "proj123"

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mbe-tools (v0.1.1)

mbe-tools is a Python toolkit for the Many-Body Expansion (MBE) workflow:

• Cluster handling: read .xyz, fragment (water heuristic or connectivity + labels), and sample fragments (random/spatial, ion-aware).
• Job prep: generate subset geometries, render Q-Chem/ORCA inputs, and emit PBS/Slurm scripts (supports chunked submission with run-control).
• Parsing: read ORCA/Q-Chem outputs, auto-detect program, infer method/basis/grid metadata, emit JSONL.
• Analysis: inclusion–exclusion MBE(k), summaries, CSV/Excel export, and quick plots.

Status: v0.1.1 (MVP) — backend syntax (e.g., ghost atoms) can be customized per site. License: MIT.

---

Install (editable for development)

cd mbe-tools
python -m pip install -e .[analysis,cli]

Settings precedence (P0)

Configure default commands/modules/scratch once and reuse across CLI calls. Precedence (low → high):

1. env vars → 2) ~/.config/mbe-tools/config.toml → 3) ./mbe.toml → 4) explicit load_settings(path=...).

Keys: qchem_command, orca_command, qchem_module, orca_module, scratch_dir, scheduler_queue, scheduler_partition, scheduler_account.

Env map: MBE_QCHEM_CMD, MBE_ORCA_CMD, MBE_QCHEM_MODULE, MBE_ORCA_MODULE, MBE_SCRATCH, MBE_SCHED_QUEUE, MBE_SCHED_PARTITION, MBE_SCHED_ACCOUNT.

Minimal mbe.toml:

qchem_command = "/opt/qchem/bin/qchem"
orca_command  = "/opt/orca/bin/orca"
qchem_module  = "qchem/5.2.2"
orca_module   = "orca/5.0.3"
scratch_dir   = "/scratch/${USER}"
scheduler_queue = "normal"
scheduler_partition = "work"
scheduler_account = "proj123"

Quickstart (Python API)

1. Fragment an XYZ

from mbe_tools.cluster import read_xyz, fragment_by_water_heuristic, fragment_by_connectivity

xyz = read_xyz("Water20.xyz")
frags = fragment_by_water_heuristic(xyz, oh_cutoff=1.25)
frags_conn = fragment_by_connectivity(xyz, scale=1.2)

2. Sample and write XYZ

from mbe_tools.cluster import sample_fragments, write_xyz

picked = sample_fragments(frags, n=10, seed=42)
write_xyz("Water10_sample.xyz", picked)

3. Generate subset geometries

from mbe_tools.mbe import MBEParams, generate_subsets_xyz

params = MBEParams(max_order=3, cp_correction=True, backend="qchem")
subset_jobs = list(generate_subsets_xyz(frags, params))  # (job_id, subset_indices, geom_text)

4. Build inputs

mbe build-input water.geom --backend qchem --method wb97m-v --basis def2-ma-qzvpp --out water_qchem.inp
mbe build-input water.geom --backend orca  --method wb97m-v --basis def2-ma-qzvpp --out water_orca.inp

5. Emit PBS/Slurm templates (run-control included)

mbe template --scheduler pbs   --backend qchem --job-name mbe-qchem --chunk-size 20 --out qchem.pbs
mbe template --scheduler slurm --backend orca  --job-name mbe-orca  --partition work --chunk-size 10 --out orca.sbatch

6. Parse outputs to JSONL

mbe parse ./Output --program auto --glob "*.out" --out parsed.jsonl

7. Analyze JSONL

mbe analyze parsed.jsonl --to-csv results.csv --to-xlsx results.xlsx --plot mbe.png

CLI cheat sheet

• mbe fragment <xyz>: water-heuristic fragmentation + sampling → XYZ. Options: --out-xyz [sample.xyz], --n [10], --seed, --require-ion, --mode [random|spatial], spatial extras --prefer-special, --k-neighbors, --start-index, --oh-cutoff.
• mbe gen <xyz>: generate subset geometries. Options: --out-dir [mbe_geoms], --max-order [2], --order/--orders, --cp/--no-cp, --scheme, --backend [qchem|orca], --oh-cutoff.
• mbe build-input <geom>: render Q-Chem/ORCA input. Options for backend, method, basis (required), charge/multiplicity, Q-Chem (--thresh, --tole, --scf-convergence, --rem-extra), ORCA (--grid, --scf-convergence, --keyword-line-extra), --out.
• mbe template: PBS/Slurm scripts with run-control wrapper. Shared: --scheduler [pbs|slurm], --backend [qchem|orca], --job-name, --walltime, --mem-gb, --chunk-size, --module, --command, --out; PBS+qchem adds --ncpus, --queue, --project; Slurm+orca adds --ncpus (cpus-per-task), --ntasks, --partition, --project (account), --qos.
• mbe parse <root>: outputs → JSONL. Options: --program [auto|qchem|orca], --glob-pattern, --out, --infer-metadata.
• mbe analyze <parsed.jsonl>: summaries/exports. Options: --to-csv, --to-xlsx, --plot, --scheme [simple|strict], --max-order.

Use mbe <command> --help for full flags.

Run-control (templates)

• Control file discovery: prefer <input>.mbe.control.toml, else mbe.control.toml, else run-control disabled.
• Attempt logging: write job._try.out; on failure rename to job.attemptN.out; on success rename to job.out. confirm.log_path can override temp log location.
• Confirmation: confirm.regex_any (must match) and confirm.regex_none (must not match) on the temp log; success also requires exit code 0.
• Retry: retry.enabled, max_attempts, sleep_seconds, cleanup_globs, write_failed_last (copy last attempt to failed_last_path).
• Delete safeguards: delete.enabled + allow_delete_outputs=true to delete outputs; inputs removed only if matched by delete_inputs_globs.
• State: .mbe_state.json records status, attempts, matched regex, log paths; skip_if_done skips reruns when marked done.

Subset naming

• Recommended: {backend}_k{order}_f{i1}-{i2}-{i3}_{cp|nocp}_{hash} with 0-based fragment indices (zero-padding allowed), e.g., qchem_k2_f000-003_cp_deadbeef.out.
• Legacy (still parsed): {backend}_k{order}_{i1}.{i2}..._{hash} treated as 1-based in the name but converted to 0-based internally. JSON always exposes subset_indices as 0-based.

JSONL schema (parse output)

{
  "job_id": "qchem_k2_f000-003_cp_deadbeef",
  "program": "qchem",
  "program_detected": "qchem",
  "status": "ok",
  "error_reason": null,
  "path": ".../job.out",
  "energy_hartree": -458.7018184,
  "cpu_seconds": 1234.5,
  "wall_seconds": 1234.5,
  "method": "wB97M-V",
  "basis": "def2-ma-QZVPP",
  "grid": "SG-2",
  "subset_size": 2,
  "subset_indices": [0, 2],
  "cp_correction": true,
  "extra": {}
}

API highlights

• Cluster (src/mbe_tools/cluster.py): read_xyz, write_xyz, fragment_by_water_heuristic, fragment_by_connectivity, sample_fragments, spatial_sample_fragments.
• MBE generation (src/mbe_tools/mbe.py): MBEParams, generate_subsets_xyz, qchem_molecule_block, orca_xyz_block.
• Input builders (src/mbe_tools/input_builder.py): render_qchem_input, render_orca_input, build_input_from_geom.
• HPC templates (src/mbe_tools/hpc_templates.py): render_pbs_qchem, render_slurm_orca (both embed run-control wrapper).
• Parsing (src/mbe_tools/parsers/io.py): detect_program, parse_files, infer_metadata_from_path, glob_paths.
• Analysis (src/mbe_tools/analysis.py): read_jsonl, summarize_by_order, compute_delta_energy, strict_mbe_orders.

Notebook

See notebooks/sample_walkthrough.ipynb for an end-to-end demo: build inputs, generate templates, and assemble MBE(k) energies from synthetic data.

License

MIT "cpu_seconds": 1234.5,

"wall_seconds": 1234.5,