Output Format

This page explains every field in the JSON files that LeMat-Synth produces.

Where are my result files?

After running any extraction script or lemat-synth extract, results are saved in a folder structure like this:

results/
└── <paper-id>/
    ├── Fe2O3.json                    ← one file per synthesized material
    ├── Ni-Fe2O3.json
    ├── performance_mappings.json     ← plot-to-material links (full pipeline only)
    └── linking_summary.json          ← counts: plots found, linked, skipped

Each <material>.json file is the main result. The other two are only written when you ran with figure extraction enabled (with_performance=true).

Note

The thermocatalysis case-study script (examples/scripts/case_study_thermocatalysis/) and run_performance_only.py write linking_summary_llm.json and linking_summary_human.json instead — the same summary plus an LLM evaluation of the links, and a blank copy for your own annotation.

The synthesis result file (`<material>.json`)

Minimal example

{
  "material": "3%Ru/CaO",
  "synthesis": {
    "target_compound": "3%Ru/CaO",
    "target_compound_type": "functional materials & catalysts",
    "synthesis_method": "wet impregnation",
    "starting_materials": [
      {
        "name": "RuCl3",
        "amount": 0.12,
        "unit": "g",
        "vendor": "Sigma-Aldrich",
        "purity": "99%"
      },
      {
        "name": "CaO",
        "amount": 1.0,
        "unit": "g",
        "vendor": null,
        "purity": null
      },
      {
        "name": "Deionized Water",
        "amount": 20.0,
        "unit": "mL",
        "vendor": null,
        "purity": null
      }
    ],
    "steps": [
      {
        "step_number": 1,
        "action": "dissolve",
        "description": "Dissolve RuCl3 in deionized water to form a precursor solution.",
        "materials": [
          {"name": "RuCl3", "amount": 0.12, "unit": "g", "vendor": null, "purity": null},
          {"name": "Deionized Water", "amount": 20.0, "unit": "mL", "vendor": null, "purity": null}
        ],
        "equipment": [{"name": "magnetic stirrer", "instrument_vendor": null, "settings": "room temperature"}],
        "conditions": {
          "temperature": null,
          "temp_unit": null,
          "duration": 30.0,
          "time_unit": "min",
          "atmosphere": "air",
          "stirring": true,
          "stirring_speed": null,
          "pressure": null,
          "pressure_unit": null,
          "ph": null
        }
      },
      {
        "step_number": 2,
        "action": "impregnate",
        "description": "Add CaO support to the precursor solution and stir.",
        "materials": [{"name": "CaO", "amount": 1.0, "unit": "g", "vendor": null, "purity": null}],
        "equipment": [],
        "conditions": {"temperature": null, "temp_unit": null, "duration": 2.0, "time_unit": "h",
                       "atmosphere": "air", "stirring": true, "stirring_speed": 300.0,
                       "pressure": null, "pressure_unit": null, "ph": null}
      },
      {
        "step_number": 3,
        "action": "dry",
        "description": "Dry the impregnated sample overnight.",
        "materials": [],
        "equipment": [{"name": "oven", "instrument_vendor": null, "settings": null}],
        "conditions": {"temperature": 110.0, "temp_unit": "C", "duration": 12.0,
                       "time_unit": "h", "atmosphere": "air", "stirring": false,
                       "stirring_speed": null, "pressure": null, "pressure_unit": null, "ph": null}
      },
      {
        "step_number": 4,
        "action": "calcine",
        "description": "Calcine the dried catalyst in air at 500 °C.",
        "materials": [],
        "equipment": [{"name": "tube furnace", "instrument_vendor": null, "settings": "5°C/min ramp"}],
        "conditions": {"temperature": 500.0, "temp_unit": "C", "duration": 4.0,
                       "time_unit": "h", "atmosphere": "air", "stirring": false,
                       "stirring_speed": null, "pressure": null, "pressure_unit": null, "ph": null}
      }
    ],
    "equipment": [
      {"name": "magnetic stirrer", "instrument_vendor": null, "settings": null},
      {"name": "oven", "instrument_vendor": null, "settings": null},
      {"name": "tube furnace", "instrument_vendor": null, "settings": "5°C/min ramp"}
    ],
    "notes": "Catalyst was reduced in H2 prior to activity measurement (not part of synthesis)."
  },
  "evaluation": {
    "reasoning": "The extraction captures the impregnation route completely and adds nothing not present in the source text.",
    "scores": {
      "structural_completeness_score": 4.5,
      "structural_completeness_reasoning": "All major fields populated; minor detail on drying oven vendor missing.",
      "material_extraction_score": 5.0,
      "material_extraction_reasoning": "Correct amounts, units, and purity values extracted.",
      "process_steps_score": 4.0,
      "process_steps_reasoning": "Steps in correct order; drying and calcination captured.",
      "equipment_extraction_score": 4.5,
      "equipment_extraction_reasoning": "Furnace and stirrer captured; oven brand missing.",
      "conditions_extraction_score": 5.0,
      "conditions_extraction_reasoning": "Temperatures, durations, and atmospheres all correct.",
      "semantic_accuracy_score": 5.0,
      "semantic_accuracy_reasoning": "All extracted information is faithful to the source.",
      "format_compliance_score": 5.0,
      "format_compliance_reasoning": "Schema fully respected.",
      "overall_score": 4.71,
      "overall_reasoning": "A faithful, near-complete extraction; only vendor-level details are missing."
    },
    "confidence_level": "high",
    "missing_information": ["Calcination ramp rate for the second batch"],
    "extraction_errors": [],
    "improvement_suggestions": ["Capture the H2 reduction step in notes rather than omitting it."]
  },
  "performance": null
}

Field-by-field explanation

Top level

Field	Type	Description
`material`	string	The material name exactly as extracted from the paper
`synthesis`	object	The structured synthesis procedure (see below)
`evaluation`	object	Quality scores from the LLM judge (see below)
`performance`	object or null	Plot-linked performance data; `null` unless `with_performance=true` was used

`synthesis` object

Field	Type	Description
`target_compound`	string	Chemical formula or name of the synthesized material
`target_compound_type`	string	One of 16 fixed categories (see table below)
`synthesis_method`	string	One of 35 fixed synthesis methods (see table below)
`starting_materials`	list	Reagents and precursors used (see `Material` below)
`steps`	list	Ordered synthesis steps (see `ProcessStep` below)
`equipment`	list	All equipment mentioned in the synthesis (see `Equipment` below)
`notes`	string or null	Anything relevant that did not fit the structured fields

`target_compound_type` — allowed values

Value	Examples
`metals & alloys`	Cu, Fe-Ni alloy, stainless steel
`ceramics & glasses`	Al₂O₃, SiO₂, BaTiO₃
`polymers & soft matter`	PDMS, PET, hydrogel
`composites`	carbon fibre / epoxy, metal matrix
`semiconductors & electronic`	GaAs, Si, InP
`nanomaterials`	Au nanoparticles, TiO₂ nanorods
`two-dimensional materials`	graphene, MoS₂ monolayer
`framework & porous materials`	MOF, zeolite, COF
`biomaterials & biological`	hydroxyapatite, collagen scaffold
`liquid materials`	ionic liquid, solution
`hybrid & organic-inorganic`	perovskite, organosilica
`functional materials & catalysts`	Pt/Al₂O₃, zeolite catalyst
`energy & sustainability`	LiFePO₄, solar cell absorber
`smart & responsive materials`	shape-memory alloy, pH-responsive gel
`emerging & quantum materials`	topological insulator, qubit
`other`	anything not fitting above

`synthesis_method` — common values

Value	Brief description
`sol-gel`	Hydrolysis/condensation of metal alkoxides
`hydrothermal`	Reaction in sealed autoclave with water at high T/P
`solvothermal`	Same but with non-aqueous solvent
`wet impregnation`	Soak support in precursor solution, dry, calcine
`incipient wetness impregnation`	Add just enough solution to fill pore volume
`precipitation` / `coprecipitation`	Precipitate from solution by pH or temperature change
`solid-state`	Mix powders and sinter/calcine
`CVD`	Chemical vapour deposition
`PVD`	Physical vapour deposition
`ball milling`	Mechanical grinding of powders
`electrochemical deposition`	Deposit material via applied current/potential
`combustion`	Auto-ignition of metal-nitrate/fuel mixture
`atomic layer deposition`	Alternating gas-phase precursor cycles
`arc melting & induction melting`	High-temperature melting under controlled atmosphere
`mechanochemical`	Solid-state reaction driven by mechanical energy
`other`	Method not in the list

`Material` object (inside `starting_materials` and `steps[].materials`)

Field	Type	Description
`name`	string	Chemical name or formula
`amount`	number or null	Numeric quantity only (e.g. `0.12`)
`unit`	string or null	Unit of amount (e.g. `"g"`, `"mL"`, `"mmol"`, `"wt%"`)
`vendor`	string or null	Supplier name if mentioned
`purity`	string or null	Purity if stated (e.g. `"99%"`, `"ACS grade"`)

`ProcessStep` object (inside `steps`)

Field	Type	Description
`step_number`	integer	Position in the sequence (starting from 1)
`action`	string	Short verb for the step — usually one of the suggested verbs below
`description`	string or null	Free-text description of the step from the paper
`materials`	list of `Material`	Materials involved in this specific step
`equipment`	list of `Equipment`	Equipment used in this step
`conditions`	`Conditions` or null	Physical conditions for this step

Suggested action values: add, mix, heat, cool, reflux, age, filter, wash, dry, reduce, calcine, dissolve, precipitate, centrifuge, sonicate, anneal, ion exchange, impregnate

Warning

Unlike synthesis_method and target_compound_type, action is not a closed enum — the model is asked to pick from this list, but the schema accepts any string, so occasional values such as "other" or "grind" do occur. If you filter or group by action, check the distinct values you actually got first: df["action"].value_counts().

`Conditions` object (inside `steps[].conditions`)

Field	Type	Description
`temperature`	number or null	Numeric value only (e.g. `500.0`)
`temp_unit`	string or null	`"C"`, `"K"`, or `"F"`
`duration`	number or null	Numeric value only
`time_unit`	string or null	`"h"`, `"min"`, `"s"`, `"days"`
`pressure`	number or null	Numeric value only
`pressure_unit`	string or null	`"atm"`, `"bar"`, `"Pa"`, `"torr"`, `"psi"`
`atmosphere`	string or null	Gas phase (e.g. `"air"`, `"N2"`, `"H2"`, `"Ar"`)
`stirring`	boolean or null	Whether stirring was used
`stirring_speed`	number or null	Speed in rpm
`ph`	number or null	pH value

A null value means the paper did not mention that condition — it does not mean the condition was absent.

`Equipment` object

Field	Type	Description
`name`	string	Instrument name (e.g. `"autoclave"`, `"tube furnace"`)
`instrument_vendor`	string or null	Manufacturer if mentioned
`settings`	string or null	Operating settings (e.g. `"heating rate 5°C/min"`)

`evaluation` object — quality scores

The seven scores live inside evaluation.scores, not directly under evaluation — so the path to the headline number is evaluation.scores.overall_score.

Field	Type	Description
`reasoning`	string	The judge’s high-level assessment, written before the scores
`scores`	object	The seven dimensions plus `overall_score` (see table below)
`confidence_level`	string	The judge’s own confidence: `"low"`, `"medium"`, `"high"`
`missing_information`	list of strings	Information present in the paper but absent from the extraction
`extraction_errors`	list of strings	Specific inaccuracies the judge found
`improvement_suggestions`	list of strings	How the extraction could be improved

evaluation itself is null when the judge was disabled or its call failed.

`evaluation.scores`

Each dimension is scored from 1 (poor) to 5 (excellent) by the LLM judge.

Field	What is being evaluated
`structural_completeness_score`	Are all schema fields populated where information exists?
`material_extraction_score`	Are names, amounts, units, and purities correct?
`process_steps_score`	Are steps in the right order and correctly classified?
`equipment_extraction_score`	Is all mentioned equipment captured?
`conditions_extraction_score`	Are temperatures, times, atmospheres, pressures correct?
`semantic_accuracy_score`	Is the meaning of each step faithfully preserved?
`format_compliance_score`	Does the output conform to the schema?
`overall_score`	Arithmetic mean of all above scores

Each score comes with a *_reasoning field explaining the rationale, and overall_score is accompanied by overall_reasoning.

Note

A low score means the extraction is incomplete or inaccurate relative to the source paper. It does not mean the synthesis itself was poor. The judge follows the rule “absence is not an error” — it will not penalise for omitting information that was never in the paper.

Performance data (`performance` field)

Only present when with_performance=true is used. Contains plot-linked data for this material.

"performance": {
  "material_name": "3%Ru/CaO",
  "plot_data": [
    {
      "plot_index": 0,
      "figure_reference": "Fig. 3a",
      "series_name": "3%Ru/CaO",
      "coordinates": [[200, 5.2], [250, 18.7], [300, 42.1], [350, 76.3], [400, 91.4]],
      "x_axis_label": "Temperature",
      "x_axis_unit": "°C",
      "y_axis_label": "CO conversion",
      "y_axis_unit": "%",
      "plot_title": "Catalytic activity vs. temperature",
      "confidence": "high"
    }
  ]
}

Field	Description
`plot_index`	Index of the figure in the paper (0-based)
`figure_reference`	Label as it appears in the paper (e.g. `"Fig. 3a"`)
`series_name`	Legend entry from the plot
`coordinates`	List of `[x, y]` pairs read from the plot
`x_axis_label` / `y_axis_label`	Axis labels
`x_axis_unit` / `y_axis_unit`	Axis units
`confidence`	Linking confidence: `"high"`, `"medium"`, or `"low"`

Reading results in Python

import json
from pathlib import Path

result_file = Path("results/my_paper/Fe2O3.json")
data = json.loads(result_file.read_text())

print(data["material"])                                  # "Fe2O3"
print(data["synthesis"]["synthesis_method"])             # "hydrothermal"
print(data["evaluation"]["scores"]["overall_score"])     # 4.3
print(data["evaluation"]["missing_information"])         # what the judge says was missed

for step in data["synthesis"]["steps"]:
    print(f"Step {step['step_number']}: {step['action']} — {step['description']}")

Python API

Troubleshooting

Lemat synth

LeMaterial

Title here

Output Format

Where are my result files?

The synthesis result file (`<material>.json`)

Minimal example

Field-by-field explanation

Top level

`synthesis` object

`target_compound_type` — allowed values

`synthesis_method` — common values

`Material` object (inside `starting_materials` and `steps[].materials`)

`ProcessStep` object (inside `steps`)

`Conditions` object (inside `steps[].conditions`)

`Equipment` object

`evaluation` object — quality scores

`evaluation.scores`

Performance data (`performance` field)

Reading results in Python

Output Format

Where are my result files?#

The synthesis result file (<material>.json)#

Minimal example#

Field-by-field explanation#

Top level#

synthesis object#

target_compound_type — allowed values#

synthesis_method — common values#

Material object (inside starting_materials and steps[].materials)#

ProcessStep object (inside steps)#

Conditions object (inside steps[].conditions)#

Equipment object#

evaluation object — quality scores#

evaluation.scores#

Performance data (performance field)#

Reading results in Python#

Where are my result files?

The synthesis result file (`<material>.json`)

Minimal example

Field-by-field explanation

Top level

`synthesis` object

`target_compound_type` — allowed values

`synthesis_method` — common values

`Material` object (inside `starting_materials` and `steps[].materials`)

`ProcessStep` object (inside `steps`)

`Conditions` object (inside `steps[].conditions`)

`Equipment` object

`evaluation` object — quality scores

`evaluation.scores`

Performance data (`performance` field)

Reading results in Python