Problem
Battery material screening is expensive when candidates are evaluated manually or without uncertainty-aware ranking.
Back to Directory
GitHub
CathodeX
AI-powered cathode material screening platform using graph neural networks for predicting battery material properties.
PythonAIGraph Neural Networks
System Architecture
Repository Evidence
Measured from GitHub public repository data on May 31, 2026.
Primary language
Python
Last public update
2026-04-13
Tracked issues
1
Repository size
24.3 MB
Language mix
PythonTypeScriptHTMLPowerShellShell
Case Study
Architecture
A web UI submits material structures to a FastAPI inference layer backed by PyTorch graph models and ensemble-style scoring.
Security Approach
Parsing, inference, and presentation are separated so untrusted input can be validated before reaching model execution and user-facing results.
Outcome
Researchers get a faster candidate-screening workflow with ranked outputs and clearer confidence signals.
Evidence
GNN-based rankingq10/q50/q90 output bandsSeparate API inference layer
Lessons Learned
- Scientific AI tools need uncertainty presentation as much as prediction.
- Keeping model execution behind an API boundary simplifies future hardening.
Technical Overview
Built using PyTorch and Graph Neural Networks (GNNs) to model the atomic structure of cathode materials. It leverages high-throughput screening algorithms to predict key battery properties such as energy density and stability.
Value Proposition
Accelerating the future of energy storage. CathodeX reduces the time and cost of battery material discovery by orders of magnitude, empowering researchers to find the next generation of sustainable energy solutions.