Polymer Database Search (Polymer-Data-Bank)

API Information

• Endpoint: POST https://open.bohrium.com/openapi/v1/database/common_data/list
• Authentication: Header accessKey: <YOUR_ACCESS_KEY>
• tableAk: 123zl00 (Polymer-Data-Bank, 280k+ records)
• Data Source: Bohrium Materials Database

Request Format

curl -s -X POST 'https://open.bohrium.com/openapi/v1/database/common_data/list' \
  -H 'Content-Type: application/json' \
  -H 'accessKey: <YOUR_ACCESS_KEY>' \
  -d '<JSON body>'

Basic Request Body

{
  "tableAk": "123zl00",
  "page": 1,
  "pageSize": 50,
  "filters": { ... },
  "selectedFields": ["field1", "field2"],
  "orderBy": [{"field": "fieldName", "order": "asc"}]
}

Parameters

Filter Structure

{
  "type": 2,
  "groupOperator": "and",
  "sub": [
    {
      "type": 1,
      "field": "field_name (dataIndex)",
      "operator": "eq",
      "value": "value"
    }
  ]
}

• type: 1 = Simple condition, 2 = Compound condition (with sub)
• groupOperator: "and" | "or"
• operator: eq / neq / like / gt / gte / lt / lte / in / nin / between

Important limitation: All property fields are string type (values like "71.3 °C", "[255 °C,288 °C]"). Operators like gt/lt perform lexicographic comparison, not numeric. Numeric range filtering must be done client-side after parsing.

Field Name Mapping

Basic Information

Thermal Properties

Optical Properties

Mechanical Properties

Electrical Properties

Molecular Weight

Query Strategies

Exact Query (Q1 type)

User asks "properties of a specific DOI/polymer": query directly with selectedFields limiting returned fields.

{
  "tableAk": "123zl00",
  "page": 1,
  "pageSize": 50,
  "filters": {
    "type": 2, "groupOperator": "and",
    "sub": [{"type": 1, "field": "doi", "operator": "eq", "value": "10.1021/..."}]
  },
  "selectedFields": ["polymer_name", "polymer_type", "GlassTransitionTemperature(Tg)", "DecompositionTemperature(Td)"]
}

Numeric Range Filtering (Q2 type)

User asks "polymers with Tg > 350°C":

1. Cannot directly use gt operator (field is string, lexicographic comparison is inaccurate)
2. Correct approach: Use like operator to exclude empty values ("value": ""), set pageSize: 5000, fetch data and parse numerically on client side

import re

def parse_value_celsius(val_str):
    """Parse temperature string with units, convert to °C"""
    if not val_str:
        return None
    nums = re.findall(r'[-+]?\d+\.?\d*', val_str)
    if not nums:
        return None
    values = [float(n) for n in nums]
    max_val = max(values)
    if 'K' in val_str and '°C' not in val_str:
        return max_val - 273.15
    elif '°F' in val_str:
        return (max_val - 32) * 5 / 9
    return max_val

3. After filtering, perform statistical analysis (type distribution, frequent monomers, etc.)

Analysis & Recommendation (Q3 type)

User asks "suggestions for synthesizing a polymer with XX properties":

1. Use like "" to fetch records with that property data (max 5000/page, may need pagination)
2. Parse values on client side, filter records meeting criteria
3. Analyze polymer_type distribution, frequent monomers in components
4. Provide structural design suggestions based on data patterns

Data Format Notes

components field format

monomer_name:SMILES;monomer_name:SMILES;...

Example: 6FDA:O=C1OC(=O)c2cc(...)ccc21;ODA:Nc1ccc(Oc2ccc(N)cc2)cc1

Property value format (all strings)

• Single value: "71.3 °C"
• Multiple values: "[255 °C,288 °C]"
• Descriptive: "over 90 %", "greater than 350 °C"
• With error: "90.8% ± 0.6%"

System fields (ignore)

• _id: MongoDB ObjectID
• a1b2c3d4e5_is_locked: Lock flag
• a1b2c3d4e5_owner_id: Data owner
• a1b2c3d4e5_source: Data source
• a1b2c3d4e5_status: Review status
• authors: Contributor info
• createTime / updateTime: Timestamps

Important Notes

1. Inconsistent units: Same field may mix °C, K, °F — unit conversion is required
2. String values: All property fields are strings, numeric comparison must be done client-side
3. Multi-value fields: A record may have multiple values for a property (array format like "[val1,val2]")
4. pageSize limit: Max 5000. For large datasets, use pagination (total count in data.count)
5. Special characters in field names: e.g. GlassTransitionTemperature(Tg) contains parentheses, use directly as JSON key
6. like empty string: "operator": "like", "value": "" matches all non-empty records
7. When presenting results to user, filter out system fields (a1b2c3d4e5_*, authors), show only meaningful data