hello-qm/catgo-lrg/adsorption

Name: adsorption
Author: hello-qm

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hello-qm/catgo-lrg
server/catgo/workflow/skills/analysis/adsorption/SKILL.md
Updated 15 Sep 2026
Version 1.0.01 version recorded

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Use when the user asks for adsorption energy, binding energy, or wants to compare how strongly a molecule binds to a surface.

Install

npx vskill@latest install hello-qm/catgo-lrg/adsorption

SKILL.md

Adsorption Energy Calculation

Theory

E_ads = E(slab+adsorbate) - E(slab) - E(adsorbate_gas)

E_ads < 0: exothermic adsorption (favorable)
E_ads > 0: endothermic (unfavorable)

With ZPE Correction

dG_ads = G(slab+adsorbate) - G(slab) - G(adsorbate_gas)

Where G includes DFT energy + ZPE - TS from Gibbs free energy calculation.

Three Required Calculations

System	Description	Notes
slab+adsorbate	Adsorbate on surface	geo_opt with fixed bottom layers
clean slab	Same slab without adsorbate	geo_opt with same settings
adsorbate gas	Isolated molecule in box	geo_opt in large vacuum box (15+ A)

All three MUST use identical computational settings (ENCUT, EDIFF, k-points for slab systems; Gamma-only for gas molecule).

MCP Workflow

Step 1: Create workflow

json

{"tool": "catgo_workflow_engine", "arguments": {
  "action": "create", "name": "CO adsorption on Pt(111)"
}}

Step 2: Build and optimize clean slab

json

{"tool": "catgo_fetch", "arguments": {
  "action": "crystal", "formula": "Pt", "source": "mp"
}}

json

{"tool": "catgo_structure", "arguments": {
  "action": "slab", "miller_index": [1,1,1],
  "min_slab_size": 12.0, "min_vacuum_size": 15.0
}}

json

{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_ads",
  "task_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "system_name": "clean_slab"}
}}

Step 3: Build and optimize slab + adsorbate

json

{"tool": "catgo_structure", "arguments": {
  "action": "add_atom", "element": "C", "position": [2.77, 1.60, 14.0]
}}

json

{"tool": "catgo_structure", "arguments": {
  "action": "add_atom", "element": "O", "position": [2.77, 1.60, 15.16]
}}

json

{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_ads",
  "task_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "system_name": "slab+CO"}
}}

Step 4: Gas-phase adsorbate

json

{"tool": "catgo_fetch", "arguments": {
  "action": "molecule", "name": "carbon monoxide"
}}

json

{"tool": "catgo_workflow_engine", "arguments": {
  "action": "add_task", "workflow_id": "wf_ads",
  "task_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "ISMEAR": 0,
             "KPOINTS": [1,1,1], "system_name": "CO_gas"}
}}

Step 5: Submit

json

{"tool": "catgo_workflow_engine", "arguments": {
  "action": "submit", "workflow_id": "wf_ads"
}}

Python API

python

from catgo.workflow import Workflow
wf = Workflow("CO adsorption on Pt(111)")
# Clean slab
slab_inp = wf.add_task("structure_input", structure=clean_slab_json)
slab_opt = wf.add_task("geo_opt", structure=slab_inp.output.structure,
                        software="vasp", ENCUT=520)
# Slab + CO
ads_inp = wf.add_task("structure_input", structure=slab_co_json)
ads_opt = wf.add_task("geo_opt", structure=ads_inp.output.structure,
                       software="vasp", ENCUT=520)
# Gas-phase CO (Gamma-only, no smearing)
co_inp = wf.add_task("structure_input", structure=co_gas_json)
co_opt = wf.add_task("geo_opt", structure=co_inp.output.structure,
                      software="vasp", ENCUT=520, ISMEAR=0,
                      KPOINTS=[1, 1, 1])
wf.submit()
# After completion:
# E_ads = ads_opt.output.energy - slab_opt.output.energy - co_opt.output.energy

With ZPE Correction

python

# Add freq + gibbs for each branch
for task_opt, name, phase in [
    (ads_opt, "slab+CO", "adsorbed"),
    (co_opt, "CO_gas", "gas"),
]:
    frq = wf.add_task("freq", structure=task_opt.output.structure,
                      software="vasp",
                      freeze_mode="layers" if phase == "adsorbed" else "none",
                      freeze_layers=4 if phase == "adsorbed" else 0)
    gib = wf.add_task("gibbs_energy", energy=task_opt.output.energy,
                      frequencies=frq.output.frequencies, phase=phase)

DAG Structure

clean_slab     --> geo_opt  ----\
slab+adsorbate --> geo_opt  ----+--> E_ads = E2 - E1 - E3
adsorbate_gas  --> geo_opt  ----/

Three independent branches, minimum 3 tasks.

Comparing Multiple Sites

To compare adsorption at different sites (top, bridge, hollow):

python

sites = {
    "top":    [2.77, 1.60, 14.0],
    "bridge": [1.39, 2.40, 13.8],
    "hollow": [1.39, 0.80, 13.6],
}
for site_name, pos in sites.items():
    inp = wf.add_task("structure_input", structure=make_ads_slab(pos))
    opt = wf.add_task("geo_opt", structure=inp.output.structure,
                      software="vasp", ENCUT=520,
                      system_name=f"CO_{site_name}")

Common Pitfalls

The gas-phase molecule must be in a large box (15+ A vacuum on all sides)

with Gamma-only k-points and Gaussian smearing (ISMEAR=0).

For dissociative adsorption (e.g., O2 --> 2 *O), use the appropriate

reference: 0.5 * E(O2_gas), not E(O_atom).

BSSE (basis set superposition error) is usually negligible for planewave

DFT but can matter for localized basis sets (CP2K GTH).

If comparing different adsorbates, always use the SAME clean slab

calculation as reference -- do not re-optimize the clean slab for each.

Check that the adsorbate did not desorb or migrate to a different site

during geo_opt by inspecting the final structure.

Security

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Source

SKILL.md in the repositoryCheckingserver/catgo/workflow/skills/analysis/adsorption/SKILL.md

Popularity

Repository stars: 199
Repository forks: 23
7-day trend score: 26

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 14 Sep 2026, a month ago.

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