README.md

metadata

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