🌍 AtomoraCQ
AI-Driven Atomic-Scale Carbon Capture Research Platform
📌 Overview
AtomoraCQ is an intelligent simulation and analysis platform designed to accelerate the discovery of efficient carbon-capture materials. By combining computational modeling, AI-based evaluation, and interactive visualization, AtomoraCQ enables researchers and innovators to study CO₂–material interactions at the atomic scale and identify promising candidates for climate action.
🎯 Problem Statement
Discovering new carbon-capture materials is traditionally slow, expensive, and computationally intensive. Researchers must test thousands of chemical structures using complex simulations, which often take months or years to validate.
💡 Solution
AtomoraCQ streamlines this process by providing:
Atomic-level simulation of CO₂ adsorption
AI-assisted material comparison
Visual performance analysis
Fast experimentation through virtual models
This reduces research time and supports data-driven material selection for carbon sequestration technologies.
✨ Key Features
🧪 Atomic Interaction Simulator Models CO₂ binding behavior with candidate materials.
🤖 AI-Based Material Evaluation Ranks and compares materials based on adsorption efficiency and stability.
📊 Performance Visualization Graphs and plots for intuitive scientific analysis.
🧠 Smart Candidate Filtering Identifies promising materials from large datasets.
🌱 Climate-Focused Design Built to support Sustainable Development Goal 13 (Climate Action).
🏗️ System Architecture
Frontend: Interactive UI for simulations and visualization
Backend: Computational models and AI evaluation engine
Data Layer: Material datasets and simulation outputs
Visualization Layer: Graphs, metrics, and performance indicators
🚀 Use Cases
Carbon capture material research
Climate-tech innovation projects
Educational molecular simulations
Hackathons and scientific demos
Early-stage material discovery pipelines
🧪 Working Simulations
AtomoraCQ includes simulated workflows for:
CO₂ adsorption modeling
Material comparison analysis
Atomic-level interaction visualization
Performance metric generation
These simulations demonstrate the feasibility of accelerated material screening.
🛠️ Tech Stack
Python
Machine Learning models
Scientific computation libraries
Data visualization tools
Web-based interface
📈 Future Scope
Integration with real quantum or HPC simulators
Expansion of material datasets
Real-time AI optimization
Laboratory data validation
Cloud-based research collaboration
🌍 Impact
AtomoraCQ contributes to:
Faster carbon-capture research
Smarter climate material discovery
Reduced experimentation cost
Scalable climate-tech innovation
Supporting SDG 13: Climate Action
📄 License
This project is licensed under the MIT License.
🤝 Contributions
Contributions are welcome. Feel free to fork this repository, create feature branches, and submit pull requests.
📬 Contact
For collaboration or research discussions: Agrim Garg Project Lead – AtomoraCQ