Curve Similarity Search extends Objective Search to compare curve-shaped outputs and find experiments with similar profiles. Instead of ranking recipes by how close they are to numeric goals, this mode evaluates shape similarity between curves and returns the most comparable results.
When to Use Curve Similarity
Use Curve Similarity when you need to:
- Identify experiments with similar signal or shape patterns, even if their absolute values differ.
- Compare spectroscopic, mechanical, or thermal data to a known performance curve.
- Find reference or baseline matches for further analysis.
Common use cases include IR and UV–Vis spectra, DMA curves, and DSC thermograms.
Accessing Curve Similarity Search
Open Objective Search from the Calculate tab.
Running a Curve Similarity Search
- Under Goals, confirm the spec output you’re searching on is a curve-type output. Only curve outputs are supported in this mode.
- Use the Target Curve dropdown to choose the reference curve you want to match. This could be a known material behavior, a previous benchmark, or a desired response profile.
Understanding the Results
After running the search, the Top Results table ranks recipes by how well they meet your spec.
- Columns show recipes, sorted by Rank (best matches on the left)
- Rows list curve similarity
- 0% means no similarity between the two curves
- 100% means the two curves are (almost) identical
How Similarity Is Calculated
Each curve is transformed into a standardized representation so comparisons reflect shape rather than scale or magnitude.
- Normalization — The x-axis is scaled to
[0, 1]and the y-axis is centered to a mean of 0. - Fourier Transform — A nonequispaced discrete Fourier transform (n = 100) captures frequency-domain features of the curve.
- Cosine Similarity — The transformed curves are compared using cosine similarity to produce a simiarity score between 0–100%.
This method emphasizes shape similarity rather than peak position or amplitude.
Minor shifts in the x-axis (e.g., slightly offset peaks) can still produce high similarity scores.
Tips and Notes
- If the Spec Type dropdown doesn’t include Curve Similarity, verify that the chosen output is a curve-type output.
- The Target Curve must have valid recorded data for the selected output.
- Filters applied in Objective Search also apply here — overly restrictive filters can limit your result set.
- Permissions still apply: if you can’t view outputs or recipes, those results won’t appear.