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Welcome to the **Spectochem** organization! We are a research-driven team dedicated to bridging the gap between Machine Learning and Computational Chemistry. 

### 🔬 Our Mission
The traditional pipeline for obtaining molecular absorption spectra relies on expensive and time-consuming quantum chemical calculations, such as Density Functional Theory (DFT). Our goal is to develop highly efficient, ML-based "virtual filters" that can rapidly estimate spectrograms and predict the physical-chemical properties of molecules without the massive computational overhead.

### 🧪 What We Do
* **Accelerating Discovery:** We train advanced predictive models to screen tens of thousands of chemical compounds, specifically focusing on identifying potential photocatalysts.
* **Graph & Molecular ML:** We leverage spatial geometries and graph representations using cutting-edge molecular machine learning frameworks.
* **Open Science:** We believe in trustworthy, transparent, and reproducible AI. All our final models, utility scripts, and pipelines are released under permissive open-source licenses (MIT/BSD).

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👥 **Contributors:** Tymoteusz Zapała, Katsiaryna Viarenich, Radosław Woźniak, Nikodem Świerkowski.  
🤝 *Collaborating with Faculty of Chemistry experts to solve real-world chemical computing challenges.*