mlmm-workflows-output

name: mlmm-workflows-output description: Output parsing and multi-step workflow selection for mlmm-toolkit — summary.json schema, R/TS/P/IM canonical paths, bond_changes interpretation, energy-diagram conventions, and the cluster + 1-step / multistep / scan-list / endpoint-MEP / TS-only / DFT//ML/MM / stage-by-stage (subcommand-only, gate each stage) recipes used to extract numerical results. TRIGGER on output parsing (summary.json, result.json, seg_NN/), extracting barriers / ΔE / Gibbs for a paper, choosing between multi-input / scan-list / endpoint-MEP / TS-only modes, or running the pipeline subcommand-by-subcommand with a success check at each stage (instead of one all run). SKIP for single-subcommand syntax (CLI skill) or install / HPC questions.

mlmm-toolkit Workflows and Output Parsing

Purpose

This skill ties the per-subcommand mds in mlmm-cli/ together into end-to-end recipes and explains how to read the resulting output trees, JSON, and figures. Use this when you have a goal ("compute the barrier of step 1 of this enzyme") and want the path through the toolkit.

mlmm-specific notes:

• Every ML/MM-evaluating subcommand requires an Amber --parm plus a layer-encoded PDB (--detect-layer from B-factor 0.0/10.0/20.0, or explicit --model-pdb/--model-indices).
• Microiteration alternates ML-region geometry steps with MM relaxation (controlled by MICROITER_KW defaults; toggled via --microiter / --no-microiter and --opt-mode hess driving the outer RFO step). The MM block uses analytical-Hessian hessian_ff, so it scales with CPU cores, not GPU.
• The mlmm dft step (when --dft is set in all) computes a single-point DFT energy on the ML region only, not the whole enzyme. The MM contribution is taken from the parm7 force field.

Six canonical workflows

1. Cluster + 1-step reaction (multi-input MEP)

You have R and P PDBs (from a published QM study). One step.

mlmm all -i 1.R.pdb 3.P.pdb \
    -c 'SAM,GPP,MG' -l 'SAM:1,GPP:-3' \
    --tsopt --thermo \
    -o result_mep

Result: result_mep/seg_NN/{reactant,ts,product}.pdb, summary.json["segments"][0]["barrier_kcal"].

2. Multi-step recursive (multi-input MEP, recursive segmentation)

You have R and P. The mechanism is multi-step, but you don't have intermediates handy. --refine-path runs the recursive path-search, which segments by detecting bond changes.

mlmm all -i 1.R.pdb 3.P.pdb \
    -c '...' -l '...' \
    --refine-path \
    --tsopt --thermo \
    -o result_mep

With --refine-path the output summary.json["n_segments"] may be > 1 — that's the recursion finding intermediates the inputs didn't contain. (Without it, single-pass path-opt yields one segment per adjacent input pair.)

3. Single-input scan-list (when only R is available)

You have just the reactant. Articulate the reaction as a sequence of distance-restraint scans.

mlmm all -i 1.R.pdb \
    -c '...' -l '...' \
    --scan-lists '[("CS1 SAM 320","GPP 321 C7",1.60)]' \
                 '[("GPP`321/H11","GLU`186/OE2",0.90)]' \
    --tsopt --thermo \
    -o result_scan

Each --scan-lists argument is one stage. See mlmm-cli/all-scan-list.md for syntax details.

4. Endpoint-MEP with explicit intermediates

You have R, IM₁, IM₂, P from the literature. Pass them all in order:

mlmm all -i 1.R.pdb 2.IM1.pdb 3.IM2.pdb 4.P.pdb \
    -c '...' -l '...' \
    --tsopt --thermo \
    -o result_mep_4pt

Add --refine-path to run recursive sub-segmentation between adjacent endpoints (then you don't have to provide every elementary step); default single-pass path-opt treats the provided endpoints as the elementary steps.

5. TS-only validation (existing TS candidate)

You have a TS guess from another code or a prior run. Skip extract / path-search:

mlmm tsopt -i ts.xyz -q -1 -m 1 -b uma -o result_tsopt
mlmm freq  -i result_tsopt/final_geometry.xyz -q -1 -m 1 -b uma -o result_freq
mlmm irc   -i result_tsopt/final_geometry.xyz -q -1 -m 1 -b uma -o result_irc

Or use mlmm all with a single -i (collapses to TS-only mode automatically; see mlmm-cli/all-ts-only.md).

6. DFT//ML/MM refinement

After any of the above, refine R / TS / P energies at DFT level:

mlmm dft -i seg_01/reactant.pdb \
    -l 'SAM:1,GPP:-3' \
    --func-basis 'wb97m-v/def2-tzvpd' \
    --engine gpu \
    -o dft_R
mlmm dft -i seg_01/ts.pdb       -l '...' --func-basis '...' -o dft_TS
mlmm dft -i seg_01/product.pdb  -l '...' --func-basis '...' -o dft_P

Composite the energies with energy-diagram (see below).

Stage-by-stage execution (subcommand-only, gate each stage)

Run the pipeline as separate subcommands instead of one mlmm all when you want to judge each stage's success before spending GPU time on the next — e.g. confirm path-search found the right segments / bond changes before optimizing a TS, or validate the TS (one imaginary mode + correct IRC connectivity) before thermo / DFT. mlmm all runs this chain (the MEP stage is single-pass path-opt by default; recursive path-search with --refine-path):

extract → [mm-parm] → path-opt → (per reactive seg) tsopt → freq → irc → [freq R/TS/P] → [dft] → energy-diagram

mlmm carry-through: every ML/MM-evaluating stage needs the same --parm + layer definition (--detect-layer on a B-factor-encoded PDB, or --model-pdb / --model-indices) and the same -l / -q / -m. Pass them on every command. After each stage, read its result.json / summary.json status and gate before continuing.

Stage 0 — prep (only from a full enzyme PDB; most staged campaigns start from already-prepared R/P cluster PDBs + parm7): generate topology, cut the pocket, encode layers with mm-parm → extract → define-layer (flags in mlmm-cli/{mm-parm,extract,define-layer}.md). GATE: real.parm7 written and the pocket PDB carries the intended ML atoms + layer B-factors (0/10/20).

Stage 1 — MEP (path-search)

mlmm path-search -i 1.R.pdb 3.P.pdb --parm real.parm7 --detect-layer \
    -l 'SAM:1,GPP:-3' -b uma -o ps/

GATE (ps/summary.json): status == "success"; inspect n_segments and EACH segment's bond_changes — the intended bonds must be formed AND broken for the right atoms (mlmm-cli/bond-summary.md, mlmm-ts-strategy/SKILL.md). Wrong segmentation / spurious changes → fix chemistry or inputs before any TS work (don't optimize a TS for the wrong step).

Stage 2 — per reactive segment: TS → validate → connectivity (seed = ps/hei_seg_NN.xyz)

mlmm tsopt -i ps/hei_seg_NN.xyz               --parm real.parm7 --detect-layer -l 'SAM:1,GPP:-3' -b uma -o seg_NN/tsopt
mlmm freq  -i seg_NN/tsopt/final_geometry.xyz --parm real.parm7 --detect-layer -l 'SAM:1,GPP:-3' -b uma -o seg_NN/freq
mlmm irc   -i seg_NN/tsopt/final_geometry.xyz --parm real.parm7 --detect-layer -l 'SAM:1,GPP:-3' -b uma -o seg_NN/irc

GATE in order: tsopt result.json status is converged (or completed; not not_converged) → freq result.json n_imaginary == 1 (exactly one imaginary frequency) whose mode moves the reacting atoms (0 or >1 → fix via fp64 / --coord-type dlc / --flatten, see mlmm-ts-strategy/SKILL.md §3, before trusting the barrier) → irc result.json status == "completed" and forward/backward endpoints connect the intended R and P (bond changes match this step). A TS that fails any gate is not this elementary step.

Stage 3 — thermochemistry (optional, = all --thermo): run mlmm freq on R / TS / P for the Gibbs/QRRHO profile (post_segments[i].gibbs_uma).

Stage 4 — DFT//ML/MM (optional, = all --dft):

mlmm dft -i seg_NN/reactant.pdb --parm real.parm7 --detect-layer -l 'SAM:1,GPP:-3' --func-basis 'wb97m-v/def2-tzvpd' -o seg_NN/dft/R   # repeat for ts, product

GATE: each dft/<state>/result.json shows "converged": true.

Stage 5 — energy diagram

mlmm energy-diagram -i 0.0 -i 21.5 -i -0.7 --label-x R --label-x TS --label-x P -o diagram.png

Resuming after a walltime hit uses these same commands — see mlmm-cli/all.md "Resume / restart". On any non-success status read summary.log, then segments/seg_NN/<stage>/result.json, before retrying. Large IRC/freq (n ≳ 4000 atoms) on a 16 GB GPU can OOM — run pysisyphus bofill_update on CPU.

summary.json schema (mlmm all output)

Top-level keys:

Per-segment keys (summary.json["segments"][i], from path-search output):

Per-segment keys in the post-processing list (summary.json["post_segments"][i], populated when --tsopt / --thermo / --dft are active):

R/TS/P canonical paths

Two locations get written for each elementary step:

result_all/
└── segments/seg_NN/                        # CANONICAL — post-LBFGS optimized
    ├── reactant.{xyz,pdb}                  # IRC backward endpoint, then LBFGS-optimized
    ├── ts.{xyz,pdb}                        # tsopt'd transition state
    ├── product.{xyz,pdb}                   # IRC forward endpoint, then LBFGS-optimized
    └── structures/
        ├── reactant.{xyz,pdb}              # same as above (canonical) — nested copy
        ├── reactant_irc.{xyz,pdb}          # raw IRC backward end (pre-LBFGS)
        ├── ts.{xyz,pdb}                    # same as above
        ├── product.{xyz,pdb}              # same as above (canonical)
        └── product_irc.{xyz,pdb}           # raw IRC forward end (pre-LBFGS)

Rule of thumb: read from segments/seg_NN/ for downstream stages. Use structures/reactant_irc.xyz / structures/product_irc.xyz only when debugging IRC vs. LBFGS divergence.

bond_changes are computed from reactant.xyz / product.xyz (post-LBFGS), not from the raw IRC endpoints.

Programmatic key extraction

import json

d = json.load(open("result_all/summary.json"))

# Per-segment MEP barriers
for seg in d["segments"]:
    print(f"seg_{seg['index']:02d} ({seg['kind']}): "
          f"ΔE‡ = {seg['barrier_kcal']:.1f} kcal/mol, "
          f"ΔE = {seg['delta_kcal']:.1f} kcal/mol")

# Rate-limiting barrier (rate_limiting_step is a dict, not an int)
rls = d.get("rate_limiting_step")
if rls is not None:
    print(f"rate-limiting: seg_{rls['segment']:02d}, "
          f"barrier = {rls['barrier_kcal']:.1f} kcal/mol "
          f"({rls['method']})")

# Post-processed (tsopt / freq / dft) per-segment data
for ps in d.get("post_segments", []):
    n_imag = ps.get("ts_imag", {}).get("n_imag")
    if n_imag is not None and n_imag != 1:
        print(f"WARNING: {ps['tag']} has {n_imag} imaginary modes at TS")

# DFT//ML/MM energies (when --dft was used)
for ps in d.get("post_segments", []):
    if "dft" in ps:
        print(ps["tag"], ps["dft"]["energies_kcal"])

Bond-change interpretation

segments[i]["bond_changes"] is a multi-line string assembled by mlmm.domain.bond_changes.summarize_changes, e.g.:

Bond formed (2):
  - C12-C7  : 3.170 Å --> 1.680 Å
  - O45-H38 : 2.410 Å --> 1.020 Å
Bond broken (2):
  - C12-S11 : 1.810 Å --> 3.050 Å
  - C7-H38  : 1.100 Å --> 2.940 Å

Reading rules:

• "Bond formed" / "Bond broken" headers are the structural change totals across the whole segment.
• For a single elementary step you usually expect 1–4 entries combined.
• Empty string for kind=="bridge" (bridge segments have no chemical bond change by construction).
• Default detection cutoff is 1.20× covalent radii (margin 0.05); see mlmm-cli/bond-summary.md.
• If a single segment shows > 8 bond changes, the recursive segmentation may have failed — inspect the geometries before trusting the barrier.

The flat {"formed": [...], "broken": [...]} dict shape is used in the irc subcommand's result, not in path_search / summary.json. Don't confuse the two.

Failed-run output

When summary.json["status"] != "success", look at:

1. summary.log — human-readable, prints the failure point first.
2. segments/seg_NN/<stage>/result.json — per-stage status (which step crashed).
3. segments/seg_NN/<stage>/<stage>.log — stack trace if any.

Even on failed runs, partial outputs are kept:

• path_opt/seg_NN/ (path_search/seg_NN/ under --refine-path) exists for any segment that completed the MEP stage (even if downstream stages failed).
• segments/seg_NN/ is only populated for fully-successful segments.

Energy diagrams

mlmm all writes path_opt/energy_diagram_*.png (path_search/... under --refine-path):

• energy_diagram_MEP.png — bare MEP energies from the path-search string (MLIP, no thermochemistry).
• energy_diagram_UMA_all.png (etc.) — per-segment energies for whichever backend was used.
• energy_diagram_G_UMA_all.png — Gibbs free-energy diagram with QRRHO thermochemistry (when --thermo).

To compose a custom diagram from energies of multiple runs, use mlmm-cli/energy-diagram.md:

mlmm energy-diagram \
    -i 0.0 -i 21.5 -i -0.7 -i 2.2 -i -18.2 \
    --label-x R --label-x TS1 --label-x IM --label-x TS2 --label-x P \
    -o my_diagram.png

See also

• mlmm-cli/all.md and the three all-*.md mode files.
• mlmm-cli/{tsopt,freq,irc,dft}.md — per-stage result.json schemas.
• mlmm-cli/bond-summary.md — same bond-change algorithm, standalone.
• mlmm-structure-io/SKILL.md — input file formats that feed these workflows.