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| title: README | |
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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.* |