Dr E. Ramanathan PhD
Interested Post Graduates can contact for Python-Enabled Spectroscopic Studies for Research Scholars in Chemistry & Physics – Advanced Track
(Emphasis on Structural Elucidation, Spectroscopic Techniques, and Spectra Interpretation)
This is an extension of the research scholar curriculum, tailored for analytical & computational research in spectroscopy, structural chemistry, and experimental physics.
Module 1 – Python Foundations for Spectroscopy
- Recap of scientific Python stack:
numpy,scipy,pandas,matplotlib,sympy - File handling: CSV, JCAMP-DX (spectroscopy), instrument-specific formats
- Noise removal and signal smoothing (Savitzky–Golay filter, FFT filtering)
Mini-task: Import an IR/UV spectrum, smooth the baseline, and normalize intensity
Module 2 – Structural Elucidation Basics
- Representation of molecules (
RDKit,Open Babel) - Parsing molecular descriptors (SMILES, InChI)
- Molecular geometry visualization (
ASE,Py3Dmol) - Correlation of structure with physical properties
Mini-task: Convert SMILES → structure → predict molecular weight and functional groups
Module 3 – Spectroscopic Techniques with Python
UV-Visible Spectroscopy
- Peak detection and λmax identification
- Beer–Lambert law fitting: absorbance vs concentration
- Band gap estimation from Tauc plots
IR Spectroscopy
- Peak deconvolution (C–H, O–H, N–H stretching regions)
- Functional group assignment via peak matching
- Heatmaps for IR fingerprint regions
NMR Spectroscopy
- Parsing 1H and 13C NMR spectra
- Integration of peaks for proton ratio determination
- Coupling constant calculations (J values)
- Automated chemical shift referencing
Mass Spectrometry
- Peak picking, isotopic pattern recognition
- Molecular ion and fragmentation analysis
- High-resolution mass data → empirical formula prediction
Mini-task: Write a Python script that reads an IR spectrum, auto-assigns key peaks, and prints functional groups present
Module 4 – Spectra Interpretation & Data Mining
- Peak fitting using
scipy.optimize.curve_fit - Spectral pattern recognition with ML models
- PCA (Principal Component Analysis) for complex mixtures
- Comparing experimental vs. simulated spectra
Mini-task (Physics): Fit Raman scattering spectrum and identify vibrational modes
Mini-task (Chemistry): Match experimental IR spectrum against a spectral library
Module 5 – Simulation of Spectra
- Quantum Chemistry Integration
- Parse Gaussian/ORCA outputs (IR, NMR, UV-Vis predictions)
- Compare simulated vs experimental spectra
- Spectral Line Shape Analysis
- Lorentzian vs Gaussian broadening
- Temperature dependence on spectra
Mini-task: Overlay simulated NMR from Gaussian with experimental data and calculate RMS error
Module 6 – Multi-Technique Structural Elucidation
- Combine NMR + IR + Mass for complete structural elucidation
- Build decision algorithms for functional group identification
- Use ML for automated spectral assignment and unknown identification
Capstone Project (Chemistry): Develop a Python tool that takes IR + NMR + MS data and suggests possible molecular structures
Capstone Project (Physics): Python-based spectral analyzer for photoluminescence or X-ray diffraction (XRD) data
Module 7 – Visualization & Reporting
- Generate publication-ready spectral plots (
matplotlib,plotly) - Automate peak table generation (wavelength, absorbance, assignment)
- Export spectra interpretation reports in LaTeX/Word
Expected Scholar Outcomes
- Automated spectral preprocessing, peak assignment, and interpretation
- Integration of computational chemistry outputs with experimental data
- Ability to simulate, compare, and validate spectra for structural elucidation
- Development of custom virtual spectroscopic labs for research groups
