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XPS (X-ray Photoelectron Spectroscopy) analysis uses a machine learning model to estimate elemental composition from survey spectra produced by an Al K-alpha source. No peak fitting or area calculations are required. The entire spectrum signature is extracted to generate a composition inference.

Overview

The XPS pipeline processes data in three stages:
  1. Data ingestion: parses uploaded spectrum files, detects energy type (kinetic vs. binding), and applies transmission correction for VAMAS files
  2. Composition inference: an ML model predicts elemental composition and uncertainty from the full spectrum signature, without peak fitting
  3. Peak matching: detected spectral peaks are matched to known experimental binding energies and assigned element and orbital labels

Key Metrics

MetricWhat It Tells You
Predicted compositionElemental atomic percentages estimated by the ML model, with uncertainty from Monte Carlo dropout.
Detected peaksBinding energy positions of identified peaks, each labeled with element and orbital (e.g., “O 1s”).
Predicted formulaStoichiometric formula derived from the predicted composition (e.g., “Al2O3”).

Adding XPS Data

Upload XPS survey spectrum files through the data management page. Supported formats: VAMAS (.vms), PHI SPE (.spe), and text (.txt, .dat). Analysis begins automatically once the upload completes, and results are typically ready within 10-30 seconds.

Viewing Results

Once processing completes, the XPS workspace shows two main sections:XPS spectrum chart: Plots intensity versus binding energy. Detected peaks appear as highlighted regions labeled with element and orbital assignments (e.g., “C 1s”, “O 1s”). You can toggle the visibility of the intensity series, peak highlight regions, and identification labels. Zoom on the binding energy axis to inspect specific regions.Element composition chart: Bar chart showing predicted atomic percentage for each element, with error bars representing the model’s uncertainty. Toggle the presence and uncertainty series independently.

Configuring Element Constraints

By default, the pipeline runs in unconstrained mode, predicting which elements are present and their concentrations. You can constrain the analysis to a specific set of elements for improved accuracy.
1

Open the configuration drawer

Click the configuration icon next to the workflow status indicator.
2

Select elements

Click cells in the interactive periodic table to select or deselect elements. You can also load a previously saved configuration from the dropdown.
3

Re-analyze

Click Re-analyze with Configuration to run the pipeline with your selected elements. Results update in about 10 seconds.
When the initial composition is predicted (unconstrained), constraining to known elements typically improves accuracy. The configuration drawer indicates when the current composition is predicted rather than confirmed.

Reanalysis

If you need to adjust element selections, open the configuration drawer, update your choices, and click re-analyze. Each re-analysis replaces the previous results.