JayLau123/Machine-learning-for-Materials
CGCNN for inorganic solid materials
This project helps materials scientists and chemists reconstruct complex molecular structures when only their spectroscopic properties are known. It takes a 1D or 2D spectrum (like from Raman spectroscopy) and iteratively builds candidate molecular graphs, representing atoms and chemical bonds. The outcome is a proposed molecular structure that best matches the observed spectrum, making it useful for researchers analyzing unknown compounds.
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Use this if you need to determine the precise atomic and bond structure of a material based solely on its spectroscopic data.
Not ideal if you already know the molecular structure and simply want to predict its properties, or if your data is not spectroscopic in nature.
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Jupyter Notebook
License
MIT
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Last pushed
Sep 07, 2025
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