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

CathodeScreen is a deployed AI system for screening lithium-ion battery

cathode 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 with

uncertainty 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/accelerating-battery-discovery-a-deep-learning-approach-to-cathode-screening-fd0e4104e33e

Content Freshness & Updates

Project Timeline

Created: January 8, 2026 at 11:37 PM (5 days ago)

Last Updated: January 13, 2026 at 10:08 PM (12 hours ago)

Update Status: Updated 0.522027233125 day ago - Recent updates

Version Information

Current Version: 1.0 (Initial Release)

Development Phase: Production Ready - Market validated and ready for acquisition

Next Update: <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&amp;D screening</p><p>- Adding user management or enterprise integrations if needed</p><p><br></p>

Activity Indicators

Project Views: 47 total views - Active engagement

Content Status: Published and publicly available

Content Freshness Summary

This project information was last updated on January 13, 2026 and represents the current state of the project. The content is very fresh and reflects recent developments.

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

PyTorch: Modern technology component for enhanced functionality and performance
AI/ML (Python: High-level programming language known for simplicity and versatility
Graph Neural Networks: Modern technology component for enhanced functionality and performance
FastAPI: Modern technology component for enhanced functionality and performance
React: Modern JavaScript library for building user interfaces with component-based architecture
"Docker": Containerization platform for consistent deployment environments
Docker Compose: Containerization platform for consistent deployment environments
Google Cloud: Modern technology component for enhanced functionality and performance
NumPy: Modern technology component for enhanced functionality and performance
Pandas: Modern technology component for enhanced functionality and performance

System Architecture & Design

Architecture Type: Other Application

Architecture Pattern: Modern Software Architecture with scalable design patterns

Scalability & Performance

Scalability Level: Standard - Scalable architecture ready for growth

Security & Compliance

Security Level: Commercial-grade security for business applications

Security Technologies: Modern security practices with component isolation and secure data handling

Data Protection: Standard data protection practices for user information and application data

Integration & API Capabilities

Live Integration: https://cathode-frontend-o4js3vzl2a-uc.a.run.app/ - Active deployment with real-world integration

API Technologies: Python API development with robust data processing capabilities

Integration Readiness: Production-ready for business integration and enterprise deployment

Development Environment & Deployment

Deployment Status: Live deployment with active user base

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&amp;D screening</p><p>- Adding user management or enterprise integrations if needed</p><p><br></p>

Technical Summary

This other project demonstrates advanced technical implementation using PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas with production-ready deployment. The technical foundation supports immediate business integration with modern security practices and scalable architecture.

Common Questions & Use Cases

How to Build a other Project Like This

Technology Stack Required: PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas

Development Approach: Build a scalable software solution with modern architecture patterns and user-centered design.

Step-by-Step Development Guide

  1. Planning Phase: Define requirements, user stories, and technical architecture
  2. Technology Setup: Configure PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas development environment
  3. Core Development: Implement main functionality and user interface
  4. Testing & Optimization: Test performance, security, and user experience
  5. Deployment: Deploy to production with monitoring and analytics
  6. Monetization: Implement revenue streams and business model

Best Practices for other Development

Technology-Specific Best Practices

PyTorch Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
AI/ML (Python Best Practices: Follow PEP 8 style guide, use virtual environments, implement proper exception handling, and optimize with profiling and caching.
Graph Neural Networks Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
FastAPI Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
React Best Practices: Use functional components with hooks, implement proper state management, follow component composition patterns, and optimize with React.memo and useMemo.
"Docker" Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
Docker Compose Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
Google Cloud Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
NumPy Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.
Pandas Best Practices: Follow modern development practices, implement proper error handling, use version control effectively, and optimize for performance and security.

General Development Best Practices

  • Code Quality: Write clean, maintainable code with proper documentation
  • Security: Implement authentication, authorization, and data protection
  • Performance: Optimize for speed, scalability, and resource efficiency
  • User Experience: Focus on intuitive design and responsive interfaces
  • Testing: Implement comprehensive testing strategies
  • Deployment: Use CI/CD pipelines and monitoring systems

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.

Comparison & Competitive Analysis

Why PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas?

This project uses PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas because:

  • Technology Synergy: The combination of PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas creates a powerful, integrated solution
  • Modern Frontend: Provides reactive, component-based user interfaces
  • Community Support: Large, active communities for ongoing development and support
  • Future-Proof: Modern technologies with long-term viability and updates

Competitive Advantages

  • Modern Tech Stack: PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas provides competitive technical advantages
  • Ready for Market: Production-ready solution with immediate deployment potential

Learning Resources & Next Steps

Learn PyTorch,AI/ML (Python,Graph Neural Networks,FastAPI,React,"Docker",Docker Compose,Google Cloud,NumPy,Pandas

To understand and work with this project, consider learning:

  • PyTorch: Official documentation and community learning resources
  • AI/ML (Python: Python documentation, tutorials, and community resources
  • Graph Neural Networks: Official documentation and community learning resources
  • FastAPI: Official documentation and community learning resources
  • React: Official React documentation, tutorials, and community resources
  • "Docker": Official documentation and community learning resources
  • Docker Compose: Official documentation and community learning resources
  • Google Cloud: Official documentation and community learning resources
  • NumPy: Official documentation and community learning resources
  • Pandas: Official documentation and community learning resources

Hands-On Learning

Try It Yourself: https://cathode-frontend-o4js3vzl2a-uc.a.run.app/

Experience the project firsthand to understand its functionality, user experience, and technical implementation. This hands-on approach provides valuable insights into real-world application development.

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,design,data

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. The architecture leverages these technologies to create a production-ready solution that can handle real-world usage scenarios.

Architecture Type: Other - This indicates the project follows modern software architecture patterns.

Technical Complexity: Multi-technology stack requiring integration expertise

Business Context & Market Position

Business Model & Revenue Potential

This project represents a other business opportunity with established market presence. The project shows strong potential for revenue generation based on its user base and market positioning.

Market Validation: With 0-45 monthly visitors, this project has achieved significant market traction and user adoption, indicating strong product-market fit.

Acquisition Opportunity: I am busy with other things and no longer have time to maintain this project. This presents an excellent opportunity for acquisition by someone looking to continue development or integrate the technology into their existing business.

Development Context & Timeline

Project Development Timeline

This project was created on January 8, 2026 and last updated on January 13, 2026. The project has been in development for approximately 0.2 months, representing 5.4598628847106 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&amp;D screening</p><p>- Adding user management or enterprise integrations if needed</p><p><br></p>

Market Readiness & Maturity

Production Readiness: This project is market-ready and has been validated through real user engagement. The codebase is stable and ready for immediate deployment or further development.

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.

Market Opportunity Assessment

Market Traction: With 0-45 monthly visitors, this project has demonstrated clear market demand and user adoption. This level of engagement indicates strong product-market fit and validates the business concept against existing market solutions.

Competitive Advantages

  • Proven Market Success: Established user base and revenue stream provide immediate competitive advantage
  • Technical Maturity: Production-ready codebase with real-world testing and optimization
  • Market Validation: User engagement and revenue data prove market demand
  • 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: 0-45

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
  • Proven user base with 0-45 monthly visitors
  • Ready for immediate acquisition

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. The price reflects the project's current revenue, user base, and market value.

What's included when I buy this project?

source_code,design,data You'll receive everything needed to run and maintain the project.

Why is the owner selling this project?

I am busy with other things and no longer have time to maintain this project. This is a common reason for selling successful side projects.

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?

The project currently has 0-45 monthly visitors. These metrics indicate the project's current performance and potential.

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.