Volcano Plot Generation
SkillDev toolsVolcano plots visualize the Sabatier principle: plotting catalytic activity (negative overpotential) against a binding energy descriptor to identify optimal catalysts at the peak of the volcano. This is the standard tool for computational catalyst screening.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Volcano Plot Generation skill
About this capability
Use when the user asks about volcano plots, catalyst screening, activity descriptors, Sabatier principle, or comparing catalyst performance across a descriptor space.
What this skill tells your AI
The instructions your AI receives, as published by hello-qm/catgo-lrg in server/catgo/workflow/skills/analysis/volcano/SKILL.md and read by ahel’s review.
Overview
Volcano plots visualize the Sabatier principle: plotting catalytic activity (negative overpotential) against a binding energy descriptor to identify optimal catalysts at the peak of the volcano. This is the standard tool for computational catalyst screening.
Key applications:
- OER/HER/ORR catalyst screening: Compare overpotentials across catalyst compositions
- Scaling relation validation: Overlay theoretical volcano lines from Norskov scaling
- Descriptor identification: Find which adsorption energy best predicts activity
- High-throughput screening: Visualize hundreds of candidates in one plot
MCP Tool: catgo_catalysis action="volcano"
Generate Volcano Plot Data
Provide a list of catalyst results with descriptor values and overpotentials:
{"tool": "catgo_catalysis", "arguments": {
"action": "volcano",
"params": {
"catalyst_results": [
{"name": "RuO2(110)", "dG_OH": 1.45, "overpotential": 0.37},
{"name": "IrO2(110)", "dG_OH": 1.52, "overpotential": 0.42},
{"name": "MnO2(110)", "dG_OH": 0.95, "overpotential": 0.68},
{"name": "TiO2(110)", "dG_OH": 2.10, "overpotential": 1.15},
{"name": "Fe-NiOOH", "dG_OH": 1.30, "overpotential": 0.32}
],
"reaction": "OER",
"descriptor_x": "dG_OH"
}
}}
Custom Descriptor Axes
Use any computed property as the x-axis descriptor:
{"tool": "catgo_catalysis", "arguments": {
"action": "volcano",
"params": {
"catalyst_results": [
{"name": "Pt(111)", "d_band_center": -2.25, "overpotential": 0.45},
{"name": "Pd(111)", "d_band_center": -1.83, "overpotential": 0.52},
{"name": "Ni(111)", "d_band_center": -1.29, "overpotential": 0.75}
],
"reaction": "HER",
"descriptor_x": "d_band_center"
}
}}
Two-Descriptor Plot
Specify both x and y descriptors explicitly (instead of using overpotential for y):
{"tool": "catgo_catalysis", "arguments": {
"action": "volcano",
"params": {
"catalyst_results": [
{"name": "RuO2", "dG_OH": 1.45, "dG_O": 2.90},
{"name": "IrO2", "dG_OH": 1.52, "dG_O": 3.10}
],
"reaction": "OER",
"descriptor_x": "dG_OH",
"descriptor_y": "dG_O"
}
}}
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| catalyst_results | list[dict] | -- | List of catalyst dicts with name, descriptor values, overpotential |
| reaction | string | "OER" | Reaction type: OER, HER, CO2RR, NRR |
| descriptor_x | string | "dG_OH" | Key for x-axis descriptor in result dicts |
| descriptor_y | string | null | Key for y-axis. If null, uses -overpotential |
Catalyst Result Dict Fields
Each dict in catalyst_results should contain:
| Field | Required | Description |
|---|---|---|
| name | yes | Catalyst identifier (plot label) |
| (descriptor_x key) | yes | X-axis value (e.g., dG_OH, d_band_center) |
| overpotential | yes* | Overpotential in V (*unless descriptor_y is set) |
Return Format
{
"points": [
{"name": "RuO2(110)", "x": 1.45, "y": -0.37, "dG_OH": 1.45, "overpotential": 0.37}
],
"ideal_line": {
"x": [0.5, 0.505, ...],
"y": [-0.23, -0.22, ...]
},
"descriptor_x": "dG_OH",
"reaction": "OER"
}
The ideal_line is generated for OER using Norskov scaling relations:
- Left branch: limited by OH adsorption (step 1)
- Right branch: limited by OOH formation (step 4), using the scaling relation dG_OOH = 0.84 * dG_OH + 3.29
For other reactions, ideal_line is null (scaling relations not
hard-coded).
Complete Workflow: OER Catalyst Screening
1. Compute overpotentials for each candidate
For each catalyst surface, run the full OER workflow (see OER skill) to obtain dG_OH, dG_O, dG_OOH, and the overpotential.
2. Collect results
Gather the results from all candidates into a list:
{"tool": "catgo_catalysis", "arguments": {
"action": "oer",
"params": {"dG_OH": 1.45, "dG_O": 2.90, "dG_OOH": 3.74}
}}
Repeat for each catalyst.
3. Generate volcano plot
{"tool": "catgo_catalysis", "arguments": {
"action": "volcano",
"params": {
"catalyst_results": [
{"name": "RuO2", "dG_OH": 1.45, "overpotential": 0.37},
{"name": "IrO2", "dG_OH": 1.52, "overpotential": 0.42}
],
"reaction": "OER",
"descriptor_x": "dG_OH"
}
}}
Common Pitfalls
- All descriptor values must use consistent DFT settings (same functional, ENCUT, k-points). Mixing PBE and RPBE results on one volcano plot produces misleading comparisons.
- The OER ideal volcano line assumes the universal OOH-OH scaling relation (dG_OOH = 0.84 * dG_OH + 3.29). This may not hold for non-oxide catalysts.
- The y-axis convention is -overpotential (higher = better catalyst). A catalyst at the peak of the volcano has the lowest overpotential.
- Catalyst results missing the descriptor_x key are silently skipped. Check that all result dicts have the expected keys.
- For HER, the typical descriptor is dG_H (hydrogen binding energy). For CO2RR, dG_CO or dG_COOH is commonly used.
Signals
- GitHub stars
- 196
- Forks
- 23
- Last commit
- Sep 2026
Others that do the same job
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volcano-plot- Source
- github.com/hello-qm/catgo-lrg