Polymer Discovery Pipeline

Discover high-performance polymers using integrated WORLD, RDKit, and BRAIN tools. Query RadonPy database for thermal conductivity data, analyze molecular structures, and predict properties with ML. Found 24 polymers with TC > 0.4 W/(m.K), top performer at 0.619 (polyimide). Structural analysis shows 100% aromatic, 1 rotatable bond. PolyNC predicted Tg = 315°C confirming high-temperature stability.

WORLD for database queries (RadonPy)
RDKit for structural analysis
BRAIN for ML predictions (PolyNC)
Full discovery pipeline integration

Chat Dialogs

Filter high thermal conductivity polymers

PROMPT: find polymers with thermal conductivity above 0.4

CONTEXT: RadonPy contains thermal conductivity data computed via Green-Kubo autocorrelation method from MD simulations. Values are in W/(m.K). High TC polymers (>0.3) are useful for thermal management applications.

THINKING: I'll query RadonPy for high thermal conductivity polymers using a WHERE filter.

RESULT:

Found 24 polymers with thermal conductivity above 0.4 W/(m.K).
Top performers from RadonPy database:

| SMILES | TC (W/m.K) | Density |
|-------------------------------------------------------------|------------|---------|
| *c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1 (PI690) | 0.619 | 1.160 |
| *c1ccc(*)s1 (PI914 - polythiophene) | 0.597 | 1.284 |
| *NC(=O)c1ccc(cc1)C(=O)Nc1ccc(cc1)c1ccc(cc1)* (PI627) | 0.576 | 1.210 |

These high-TC polymers are candidates for thermal interface materials.

View technical details

Analyze top thermal conductor structure

PROMPT: The top thermal conductor looks interesting. Can you analyze its molecular structure? I want to understand why it has such high thermal conductivity.

CONTEXT: The polymer PI690 (*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1) is a polyimide with imide rings and aromatic backbone - known for high thermal conductivity.

THINKING: I'll run a complete structural analysis using RDKit cheminformatics tools: calculate_molecular_weight, calculate_aromatic_proportion, count_rotatable_bonds.

RESULT:

Polyimide (PI690) - Top Thermal Conductor
SMILES: *c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1

Structural Analysis:
| Property | Value |
|---------------------|-----------------------------|
| Molecular weight | 304.26 g/mol |
| Aromatic rings | 4 |
| Aromatic proportion | 100% (fully aromatic) |
| Rotatable bonds | 1 (very rigid backbone) |
| TC from RadonPy | 0.619 W/(m.K) |
| Density | 1.160 g/cm³ |

Key features explaining high thermal conductivity:
- Fully aromatic structure: 100% aromatic rings for efficient phonon transport
- Only 1 rotatable bond: extremely rigid backbone
- Imide groups provide strong intermolecular interactions
- Polyimide class: known for excellent thermal properties

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Predict Tg with ML model

PROMPT: Can you predict the glass transition temperature for this polyimide using an ML model?

CONTEXT: BRAIN platform hosts PolyNC model for Tg prediction. This validates the expected high-temperature stability of polyimides.

THINKING: I'll use PolyNC on BRAIN to predict Tg for the top thermal conductor.

RESULT:

Using PolyNC (Neural Network) for Tg prediction...

SMILES: *c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1

PolyNC Prediction:
| Property | Value |
|-------------------|-------------------------|
| Predicted Tg | 315°C (588K) |
| Computation time | 2700ms |
| Model version | PolyNC v1.3.0 |
| Validation | in_training_range: true |

High Tg (315°C) confirms this polyimide is excellent for:
- High-temperature electronics
- Thermal interface materials
- Aerospace applications

View technical details

Compare with other high-TC polymers

PROMPT: Can you compare the structural features of the top 3 thermal conductors?

CONTEXT: Comparing structural features across high-TC polymers helps identify design principles for thermal materials.

THINKING: I'll analyze all three top performers and compare their molecular properties.

RESULT:

Structural Comparison - Top 3 Thermal Conductors:

| Polymer | TC (W/m.K) | MW (g/mol) | Aromatic % | Rotatable |
|---------|------------|------------|------------|-----------|
| PI690 | 0.619 | 304.26 | 100% | 1 |
| PI914 | 0.597 | 82.13 | 100% | 0 |
| PI627 | 0.576 | 314.34 | 100% | 4 |

Key Design Principles:
- High aromaticity (>85%): enables efficient phonon transport
- Low rotatable bonds: rigid chains reduce phonon scattering
- Conjugated systems: polythiophene (*c1ccc(*)s1) is fully conjugated
- Imide/amide groups: strong intermolecular bonding improves heat transfer

Recommendation: For high thermal conductivity, prioritize:
1. Fully aromatic backbone
2. Minimal rotatable bonds
3. Strong intermolecular interactions (H-bonding, π-stacking)

View technical details

Technical Details

Thermal Conductivity Query

INPUT:

{
  "dataset_id": "radonpy",
  "query": "SELECT smiles, thermal_conductivity, density, monomer_ID FROM radonpy WHERE thermal_conductivity > 0.4 ORDER BY thermal_conductivity DESC LIMIT 10"
}

OUTPUT:

{
  "success": true,
  "data": [
    {"smiles": "*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1", "thermal_conductivity": 0.619, "density": 1.160, "monomer_ID": "PI690"},
    {"smiles": "*c1ccc(*)s1", "thermal_conductivity": 0.597, "density": 1.284, "monomer_ID": "PI914"},
    {"smiles": "*NC(=O)c1ccc(cc1)C(=O)Nc1ccc(cc1)c1ccc(cc1)*", "thermal_conductivity": 0.576, "density": 1.210, "monomer_ID": "PI627"}
  ],
  "rows_returned": 24,
  "execution_time_ms": 371
}

RDKit Structural Analysis

INPUT (calculate_molecular_weight):

{
  "smiles": "*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1"
}

OUTPUT:

Molecular weight: 304.26 g/mol

INPUT (calculate_aromatic_proportion):

{
  "smiles": "*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1"
}

OUTPUT:

{
  "aromatic_rings": 4,
  "total_rings": 4,
  "aromatic_proportion": 1.0
}

INPUT (count_rotatable_bonds):

{
  "smiles": "*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1"
}

OUTPUT:

Rotatable bonds: 1

PolyNC Tg Prediction

INPUT:

{
  "service_name": "PolyNC",
  "input_data": {
    "input_type": "text",
    "data": {
      "text": "*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1",
      "property_type": "Tg"
    }
  }
}

OUTPUT:

{
  "success": true,
  "service_name": "PolyNC",
  "port": 5102,
  "prediction": {
    "success": true,
    "model_name": "PolyNC",
    "results": {
      "prediction": "315",
      "property_type": "Tg",
      "smiles": "*c1ccc(-n2c(=O)c3cc4c(=O)n(*)c(=O)c4cc3c2=O)c(C)c1"
    },
    "computation_time_ms": 2699.664,
    "validation": {
      "in_training_range": true
    }
  }
}

Polymer Comparison Analysis

Polythiophene Analysis (PI914):

SMILES: *c1ccc(*)s1
Molecular weight: 82.13 g/mol
Aromatic proportion: 100%
Rotatable bonds: 0
Thermal conductivity: 0.597 W/(m.K)

Aramid Analysis (PI627):

SMILES: *NC(=O)c1ccc(cc1)C(=O)Nc1ccc(cc1)c1ccc(cc1)*
Molecular weight: 314.34 g/mol
Aromatic proportion: 100%
Rotatable bonds: 4
Thermal conductivity: 0.576 W/(m.K)

Tools Used

world_dataset_query calculate_molecular_weight calculate_aromatic_proportion count_rotatable_bonds ai_list_services ai_predict

Comparing Polymer Structures with AI

Chat Dialogs

Filter high thermal conductivity polymers

Analyze top thermal conductor structure

Predict Tg with ML model

Compare with other high-TC polymers

Technical Details

Thermal Conductivity Query

RDKit Structural Analysis

PolyNC Tg Prediction

Polymer Comparison Analysis

Tools Used

Outcomes