CathodeScreen | Enterprise Material Discovery - Sell Project
Project Overview
CathodeScreen is a deployed AI system for screening lithium-ion batterycathode materials using graph neural networks trained on DFT data.The project includes a production-ready frontend, FastAPI backend,and trained deep ensemble models that predict energy-above-hull withuncertainty estimates, enabling fast pre-DFT material filtering.Ideal for research teams, materials startups, or internal R&D use.For deeper understandig you can read the Medium article below: https://medium.com/@erenari27/ac...
Detailed Description
Visual Content & Media
Project Screenshots & Interface
The following screenshots showcase the visual design and user interface of CathodeScreen | Enterprise Material Discovery:
Screenshot 1: Main Dashboard & Primary Interface
This screenshot displays the main dashboard and primary user interface of the application, showing the overall layout, navigation elements, and core functionality. The interface demonstrates the modern design principles and user experience patterns implemented using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas.
Screenshot 2: Key Features & Functionality
This screenshot displays key features and functionality of the application, demonstrating specific capabilities and user interactions. The interface demonstrates the modern design principles and user experience patterns implemented using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas.
Screenshot 3: User Experience & Navigation
This screenshot displays user experience elements and navigation patterns, showing how users interact with the interface. The interface demonstrates the modern design principles and user experience patterns implemented using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas.
Screenshot 4: Advanced Features & Capabilities
This screenshot displays additional features and advanced capabilities, showcasing the full scope of the application. The interface demonstrates the modern design principles and user experience patterns implemented using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas.
Project Demonstration Videos
The following videos provide visual demonstrations of CathodeScreen | Enterprise Material Discovery in action:
Demo Video 1: Main Functionality Walkthrough
This video demonstrates the main functionality and core features of the application, providing a comprehensive overview of how the system works. The video showcases the other application's technical implementation using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas and user interface design, providing viewers with a clear understanding of the project's capabilities and value proposition.
Video URL: https://www.youtube.com/watch?v=xuT4muMGHOc
Live Demo & Interactive Experience
Live Demo URL: https://cathode-frontend-o4js3vzl2a-uc.a.run.app/
Experience CathodeScreen | Enterprise Material Discovery firsthand through the live demo. This interactive demonstration allows you to explore the application's features, test its functionality, and understand its user experience. The live demo showcases the other application's technical capabilities implemented with PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas and real-world performance, providing a comprehensive understanding of the project's value and potential.
Visual Content Summary
This project includes 4 screenshots and 1 demonstration video plus a live demo, providing comprehensive visual documentation of the other application. The media content demonstrates the project's technical implementation using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas and user interface design, showcasing both the visual appeal and functional capabilities of the solution.
Technical Specifications & Architecture
Technology Stack & Implementation
Primary Technologies: PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas
Technology Count: 10 different technologies integrated
Implementation Complexity: High - Multi-technology stack requiring extensive integration expertise
Technology Analysis
Common Questions & Use Cases
Use Cases & Practical Applications
Target Audience & Use Cases
Business Use Cases: This project is ideal for businesses looking to implement a ready-made solution. Perfect for entrepreneurs, startups, or established companies seeking other solutions.
Project Details
Project Type: Other
Listing Type: Sell
Technology Stack: PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas
What's Included
Source code, All related design work, All related data
Included in the sale: Full frontend and backend source code Trained model weights for inference Docker-based deployment configuration Basic setup and run documentation
Reason for Selling
I am busy with other things and no longer have time to maintain this project.
Technical Architecture
Technology Stack & Architecture
This other project is built using a modern technology stack consisting of PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas.
Architecture Type: Other - This indicates the project follows modern software architecture patterns.
Technical Complexity: Multi-technology stack requiring integration expertise
Business Context & Market Position
Development Context & Timeline
Project Development Timeline
This project was created on January 8, 2026 and last updated on September 13, 2026. The project has been in development for approximately 8.3 months, representing 248.08111145683 days of development time.
Technical Implementation Effort
Implementation Complexity: High - The project uses 10 different technologies (PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas), requiring extensive integration work and cross-technology expertise.
Next Development Phase: <p>The buyer can extend the system by:</p><p>- Training on additional materials datasets</p><p>- Integrating active learning or DFT feedback loops</p><p>- Improving model architectures or uncertainty calibration</p><p>- Deploying it internally for large-scale R&D screening</p><p>- Adding user management or enterprise integrations if needed</p><p><br></p>
Competitive Analysis & Market Position
Market Differentiation
Technology Advantage: This project leverages PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas to create a unique solution in the other space. The technology stack provides modern, reactive user interfaces that sets it apart from traditional solutions.
Competitive Advantages
- Modern Technology Stack: PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas provides scalability, maintainability, and future-proofing
Pricing Information
Offer Price: $2,500 USD
Project Metrics
Average Monthly Visitors: Under 1K/mo
Average Monthly Revenue: Undisclosed
About the Creator
Developer: User ID 209198
Project Links
Live Demo: https://cathode-frontend-o4js3vzl2a-uc.a.run.app/
Key Features
- Built with modern technologies: PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas
- Listed for sale
Frequently Asked Questions
What is this project about?
CathodeScreen | Enterprise Material Discovery is a other project that CathodeScreen is a deployed AI system for screening lithium-ion batterycathode materials using graph neural networks trained on DFT data.The project includes a production-ready frontend, FastAPI backe....
How much does this project cost?
This project is listed for sale at $minimum USD. There's also an offer price of $2,500 USD.
What's included when I buy this project?
Source code, All related design work, All related data.
Why is the owner selling this project?
I am busy with other things and no longer have time to maintain this project.
What technologies does this project use?
This project is built with PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas. These technologies were chosen for their suitability to the project's requirements and the developer's expertise.
What are the project's current metrics?
Monthly visitors: Under 1K/mo. Monthly revenue: Undisclosed.
Can I see a live demo of this project?
Yes! You can view the live demo at https://cathode-frontend-o4js3vzl2a-uc.a.run.app/. This will give you a better understanding of the project's functionality and user experience.
How do I contact the project owner?
You can contact the project owner through SideProjectors' messaging system. Click the "Contact" button on the project page to start a conversation about this project.
Is this project still actively maintained?
Since this project is for sale, the current owner may be looking to transfer maintenance responsibilities to the buyer.