readme.md

AURELIUS Battery Optimizer

🔬 The Problem

Traditional materials discovery is slow. Simulating the stability of a doped battery material usually requires setting up complex DFT calculations or managing messy scripts.

🚀 The Solution

Aurelius is a microservice architecture that allows researchers to screen thousands of doping strategies in seconds.

• Universal Physics Engine: Dynamically calculates Vegard's Law lattice strain and electronegativity mismatches for any host-dopant system using periodic table data (mendeleev).
• Real-Time Data Integration: Connects to the Materials Project API to fetch ground-truth thermodynamic properties (Band Gap, Volume) for the host material.
• High-Entropy Support: Capable of simulating complex co-doping recipes (mixtures of 2+ elements) to predict stability in high-entropy configurations.

🛠️ Engineering Architecture

This is not just a script; it is a production-ready system designed for scale.

• Backend: FastAPI (Python) serving an async REST API.
• Streaming: Implements NDJSON Streaming to deliver inference results row-by-row, preventing Gateway Timeouts during large batch processing.
• Resilience: Features a "Graceful Degradation" adapter that switches to theoretical estimation if the external Materials Project API is down or the material is novel.
• Deployment: Fully Dockerized container serving both the API and a lightweight static UI.

💻 Usage

1. Enter a host formula (e.g., Li3PS4).
2. Define a batch of doping recipes (JSON).
3. The system streams stability predictions back in real-time.