---
title: CNT AI Pipeline Platform
emoji: ⚗️
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
short_description: AI-driven Fe(Cp)2 → CNT manufacturing digital twin
---

# ⚗️ Fe(Cp)₂ → CNT · AI Platform

**AI-Driven Carbon Nanotube Manufacturing Simulator**

An interactive digital twin platform for CNT synthesis optimization, combining ReaxFF molecular dynamics simulation data with a 5-stage ML pipeline for predicting and optimizing CNT manufacturing conditions.

## Features

### ⚡ Digital Twin Reactor
- 3D interactive Plotly visualization of ferrocene molecular dynamics
- Temperature-dependent bond evolution (200–2000 K)
- Live reactor metrics: pressure, potential/kinetic energy, bond statistics
- Real-time cluster analysis and CNT potential scoring
- **NEW:** Multi-catalyst support (Fe, Fe-C, Fe-S, Fe-Mo-C, Fe-Co-C, Fe-Ni-C)

### 🎬 Decomposition Analysis
- 6-frame molecular decomposition movie (Intact → Catalyst Nanoparticle)
- Bond order vs temperature animated chart (Fe–Cp, C–C, C–H)
- Bond survival landscape heatmap
- Decomposition pathway with thermal thresholds

### 🔵 Catalyst & CNT Predictor
- Fe nanoparticle cluster growth trajectory simulation
- CNT growth predictor with interactive input sliders
- Semi-circle gauge chart for nucleation probability
- T vs Cluster Size nucleation probability heatmap

### 🌳 Pathways & Summary
- Sankey diagram of reaction pathway tree
- Pathway branching probabilities
- Pipeline completion status
- Executive summary dashboard (91 simulation runs, 13.6M timesteps)

### 🤖 AI Pipeline
- **NEW:** 8,000-row synthetic DI-FCCVD dataset with **6 catalyst types** (Fe, Fe-C, Fe-S, Fe-Mo-C, Fe-Co-C, Fe-Ni-C)
- **NEW:** Multi-product support (SWCNT, DWCNT, MWCNT)
- **NEW:** Catalyst composition tracking (Mo, Co, Ni promoters in ppm)
- 5-stage ML cascade (Random Forest, R² 0.88–0.96)
- Feature correlation heatmap
- Bayesian optimization — Top 5 synthesis recipes
- **NEW:** CNT type and catalyst type distribution pie charts
- Downloadable master dataset

### ⚙️ ReaxFF Optimization (**NEW TAB**)
- **CMA-ES optimization simulation** with loss function evolution
- Parameter subset optimization (Bond → vdW → Angle → Off-diagonal)
- Energy/Force R² metrics and RMSE tracking
- **CNT nucleation probability calculator** with multi-catalyst comparison
- **Arrhenius plot** comparing all 6 catalyst types
- Activation energy barriers by catalyst (1.6–2.1 eV)
- DFT database statistics (300+ calculations, 3,000+ entries)
- Configuration types: Supercells, vacancies, strain, substitutions, interstitials, slabs

## Pipeline Architecture

```
Public Data + Synthetic DI-FCCVD Data
  → Data Cleaning & Feature Engineering
  → Model 1: Atomistic Catalyst (decomposition_rate)
  → Model 2: Fe NP Formation (NP_size_nm)
  → Model 3: CNT Growth (cnt_growth_prob)
  → Model 4: Reactor Surrogate (residence_time_s)
  → Model 5: CNT Quality (purity, yield, diameter)
  → Bayesian Optimization → Best Recipe
```

## Key Results

- **Decomposition onset**: T ≈ 900–1100 K (Fe–Cp bond order < 0.3)
- **Optimal catalyst**: Fe₅ nanoparticle, ~0.75 nm radius
- **SWCNT range**: 1–5 nm catalyst clusters (3–15 Fe atoms)
- **GPU acceleration**: 38× speedup vs CPU baseline
- **Dataset**: 91 temperature points, 13.6M ReaxFF timesteps
- **NEW:** Best catalyst for nucleation: **Fe-Mo-C** (E_a = 1.6 eV)
- **NEW:** Multi-catalyst comparison across 6 compositions
- **NEW:** DFT-trained ReaxFF optimization: Energy R² = 0.293, Force R² = 0.377

## Recent Improvements (v2.0)

### Multi-Catalyst Support
- Added 6 catalyst types: Fe, Fe-C, Fe-S, Fe-Mo-C, Fe-Co-C, Fe-Ni-C
- Tracked promoter metals (Mo, Co, Ni) in ppm
- Catalyst-specific activation energy barriers (1.6–2.1 eV)

### Multi-Product CNT Types
- SWCNT (Single-Wall)
- DWCNT (Double-Wall)  
- MWCNT (Multi-Wall with 3–15 layers)
- Ultra-Long CNT, CNT Fiber, Conductive Network, High-Purity targets

### ReaxFF Optimization Module (Tab 6)
- CMA-ES genetic algorithm visualization
- Parameter subset optimization workflow
- CNT nucleation probability calculator
- Multi-catalyst Arrhenius comparison
- DFT training database statistics

### Enhanced AI Pipeline (Tab 5)
- CNT type distribution pie chart
- Catalyst composition distribution chart
- Expanded dataset from 14 to 21 feature columns
- Nucleation barrier tracking by catalyst