WORKSHOP
“AI for Spectroscopy and Characterisation”
We bring together leading academic scientists and researchers!
November 13th, 2026 1pm - 5pm
Scope of the event
Vibrational spectroscopy (infrared, Raman), X-ray diffraction, nuclear magnetic resonance, and electron microscopy are the primary experimental windows through which materials scientists and chemists observe struc- ture, bonding, and composition. Machine learning is transforming each of these characterisation techniques: automating phase identification, deconvoluting overlapping signals, predicting spectra from first-principles calculations, and enabling the solution of inverse problems — inferring structure from spectral data.
This workshop examines these developments with worked examples drawn from inorganic chemistry and mate- rials characterisation. It addresses convolutional neural networks for automated phase identification from X-ray diffraction patterns, ML-assisted deconvolution of complex vibrational spectra, graph neural network and ML potential approaches to predicting NMR chemical shifts and IR/Raman spectra, and deep learning methods for electron microscopy image analysis including defect detection and atomic structure reconstruction.
The session also addresses the practical challenge of transfer learning — adapting models trained on simulated spectra to experimental data with different noise characteristics, resolution, and baseline artefacts — a problem of particular importance given that training data are often generated computationally while deployment targets experimental measurements. Participants will gain practical guidance on applying ML to their own characterisation data and a clear understanding of where these methods are mature and where significant challenges remain.
Extended Key Takeaways & Outputs
• Practical knowledge of ML approaches for XRD, vibrational spectroscopy, NMR, and electron microscopy
• Understanding of inverse problems and structure-from-spectrum inference
• Strategies for transfer learning between simulated and experimental spectral data
• Worked examples from inorganic chemistry and materials characterisation
• Assessment of maturity and remaining challenges for ML-assisted characterization
Agenda & Speakers
13:00–13:05 — Introduction & Objectives
Organizer
· Role of spectroscopy and characterisation in materials science and chemistry
· From data interpretation to AI-assisted analysis and inverse modelling
· Workshop objectives: automation, accuracy, and structure identification
13:05–13:35 — Machine Learning for X-ray Diffraction and Phase Identification
Speaker (Academia): N.N.
· Automated phase identification from XRD patterns
· Convolutional neural networks for crystallographic analysis
13:35–14:05 — ML for Vibrational Spectroscopy: IR and Raman Analysis
Speaker (Academia): N.N.
· Deconvolution of complex vibrational spectra
· Predicting IR and Raman spectra from atomic structure
14:05–14:35 — NMR Prediction and Structure Inference Using ML
Speaker (Academia): N.N.
· ML prediction of NMR chemical shifts
· Linking structure and spectral signatures
14:35–14:45 — Break
14:45–15:15 — Electron Microscopy and Image-Based Characterisation
Speaker (Industry): N.N.
· Deep learning for defect detection and atomic reconstruction
· Image analysis for high-resolution microscopy data
15:15–15:45 — Transfer Learning: From Simulated to Experimental Data
Speaker (Industry): N.N.
· Bridging simulation-trained models with real experimental data
· Handling noise, resolution differences, and artefacts
15:45–16:15 — Inverse Problems: From Spectra to Structure
Speaker (Policy/Applied Research): N.N.
· Inferring structure from spectral and imaging data
· Challenges in uniqueness, uncertainty, and validation
16:15–16:55 — Panel Discussion & Strategic Alignment
All Speakers + Moderator
· How far can AI automate materials characterisation?
· Balancing accuracy, interpretability, and experimental constraints
· Integration into laboratory workflows and instrumentation
· Future outlook: autonomous characterisation and real-time analysis
Concluding remarks
Registration
Deadlines:
Early-Bird: until July 15th, 2026
Registration : until September 5th, 2026
The registration takes place online by filling out the form.
The registration and access to the virtual room for workshop is effective only if the fees payment has been confirmed.
Please note that INVIRTA MEMBERS can purchase all our workshops and services with 10% discount . If you desire to get more information on our membership please press here .
Prices* :
Industry: 150 € (early bird**: 125 €)
Academy: 125 € (early bird**: 100 €)
Students***: 90 € (early bird**: 75 €)
* Note that prices are subject to VAT (for European countries, except Germany, provide the VAT number of your organisation to be within the VAT reverse framework payment).
** Until November 15th, 2020
*** Copy of the registration certificate (Ph.D, undergraduates)
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