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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title Combined THz-TDS and Raman Spectroscopy for In-Situ Material Identification via a Machine Learning Algorithm
Author(s) Marco Herbsommer, Sushrut Karmarkar, Mahavir Singh, Meghana Sudarshan, Vikas Tomar
On-Site Speaker (Planned) Marco Herbsommer
Abstract Scope In-situ material identification was achieved by combining spectroscopy and machine learning to serve as a proof of concept for a rapid method of recyclable plastic sorting. The study stemmed from a need for rapid in-situ material identification by the petrochemical industry. Terahertz time-domain spectroscopy (THz-TDS) was used to measure the spectral signature of polymers in the terahertz range for common recyclable plastics. The spectral identification of each of the polymers was confirmed by a secondary Raman measurement for the polymer sample done concurrently with the THz-TDS measurement. A machine learning model was trained to identify specific absorption peaks and resonances as the spectrums reveal the material’s unique response to electromagnetic radiation. This was to avoid time-consuming and computationally intensive database comparisons. A framework for creating a database of material responses with terahertz and Raman spectroscopy was generated and initial ML model testing results served as a proof of concept.
Proceedings Inclusion? Planned:
Keywords Polymers, Characterization, Computational Materials Science & Engineering

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Combined THz-TDS and Raman Spectroscopy for In-Situ Material Identification via a Machine Learning Algorithm
Conditional Diffusion Models for Interlocking Metasurface Design
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