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Catalysis

Predict adsorption energy of a molecule on a substrate surface using ALIGNN force-field. Provide the clean substrate POSCAR and the substrate+adsorbate POSCAR, specify which atoms are the adsorbate (1-indexed), and the app computes E_ads = E_cat − E_sub + μ(adsorbate). Chemical potentials are from JARVIS DFT elemental reference energies. Supports default and wt01 ALIGNN-FF models.

Open App

Overview

The Catalysis app computes adsorption energies without running full DFT. It uses ALIGNN-FF (AlignnAtomwiseCalculator) to evaluate the total energy of both the clean substrate and the substrate+adsorbate system, then combines them with JARVIS elemental chemical potentials:

E_ads = E_catalyst − E_substrate + μ(adsorbate atoms)

where μ is the sum of per-atom elemental reference energies from jarvis.analysis.thermodynamics.energetics.get_optb88vdw_energy(). Negative E_ads indicates exothermic (favorable) adsorption.

Data Source

ALIGNN-FF (default_path or wt01_path) for energy calculations. JARVIS-DFT elemental reference chemical potentials.

Endpoints

POST /catalysis/predict — Compute adsorption energy

Takes two POSCAR structures (substrate and catalyst) plus adsorbate atom indices. Computes energies with ALIGNN-FF and returns the adsorption energy breakdown.

curl -X POST "https://atomgpt.org/catalysis/predict" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "substrate": "Substrate\n1.0\n5.882 0.0 0.0\n2.941 5.094 0.0\n0.0 0.0 22.203\nAg Au\n12 4\ndirect\n0.0 0.0 0.338\n0.5 0.0 0.338\n0.0 0.5 0.338\n0.5 0.5 0.338\n0.167 0.167 0.446\n0.667 0.167 0.446\n0.167 0.667 0.446\n0.667 0.667 0.446\n-0.167 0.333 0.554\n0.333 0.333 0.554\n-0.167 0.833 0.554\n0.333 0.833 0.554\n0.0 0.0 0.662\n0.5 0.0 0.662\n0.0 0.5 0.662\n0.5 0.5 0.662",
    "catalyst": "Catalyst,adsorbate_indices:[17]\n1.0\n5.882 0.0 0.0\n2.941 5.094 0.0\n0.0 0.0 22.203\nAg Au O\n12 4 1\ndirect\n0.0 0.0 0.338\n0.5 0.0 0.338\n0.0 0.5 0.338\n0.5 0.5 0.338\n0.167 0.167 0.446\n0.667 0.167 0.446\n0.167 0.667 0.446\n0.667 0.667 0.446\n-0.167 0.333 0.554\n0.333 0.333 0.554\n-0.167 0.833 0.554\n0.333 0.833 0.554\n0.0 0.0 0.662\n0.5 0.0 0.662\n0.0 0.5 0.662\n0.5 0.5 0.662\n0.0 0.0 0.743",
    "adsorbate_indices": [17],
    "model": "default"
  }'

Multi-atom adsorbate (CO on Cu):

curl -X POST "https://atomgpt.org/catalysis/predict" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "substrate": "Cu\n1.0\n3.615 0.0 0.0\n0.0 3.615 0.0\n0.0 0.0 3.615\nCu\n4\ndirect\n0.0 0.0 0.0\n0.0 0.5 0.5\n0.5 0.0 0.5\n0.5 0.5 0.0",
    "catalyst": "Cu,adsorbate_indices:[5,6]\n1.0\n3.615 0.0 0.0\n0.0 3.615 0.0\n0.0 0.0 3.615\nCu C O\n4 1 1\ndirect\n0.0 0.0 0.0\n0.0 0.5 0.5\n0.5 0.0 0.5\n0.5 0.5 0.0\n0.0 0.0 0.2\n0.0 0.0 0.35",
    "adsorbate_indices": [5, 6],
    "model": "default"
  }'
Field Type Default Description
substrate string required POSCAR of clean substrate (no adsorbate)
catalyst string required POSCAR of substrate + adsorbate
adsorbate_indices list[int] required 1-indexed atom positions of adsorbate in catalyst POSCAR
model string "default" ALIGNN-FF model: "default" or "wt01"

Auto-detection

The web UI auto-detects adsorbate indices from the POSCAR comment line format adsorbate_indices:[17].

Response:

Field Description
adsorption_energy E_ads (eV) — negative = exothermic
substrate_energy E_substrate from ALIGNN-FF (eV)
catalyst_energy E_catalyst from ALIGNN-FF (eV)
chemical_potential Sum of elemental μ for adsorbate atoms (eV)
chemical_potential_details Per-element breakdown
formula_substrate Reduced formula of substrate
formula_catalyst Reduced formula of catalyst
adsorbate_elements List of adsorbate element symbols
result_text Human-readable summary

Python Examples

import requests

SUBSTRATE = """Substrate
1.0
5.882 0.0 0.0
2.941 5.094 0.0
0.0 0.0 22.203
Ag Au
12 4
direct
0.0 0.0 0.338
0.5 0.0 0.338
0.0 0.5 0.338
0.5 0.5 0.338
0.167 0.167 0.446
0.667 0.167 0.446
0.167 0.667 0.446
0.667 0.667 0.446
-0.167 0.333 0.554
0.333 0.333 0.554
-0.167 0.833 0.554
0.333 0.833 0.554
0.0 0.0 0.662
0.5 0.0 0.662
0.0 0.5 0.662
0.5 0.5 0.662"""

CATALYST = """Catalyst,adsorbate_indices:[17]
1.0
5.882 0.0 0.0
2.941 5.094 0.0
0.0 0.0 22.203
Ag Au O
12 4 1
direct
0.0 0.0 0.338
0.5 0.0 0.338
0.0 0.5 0.338
0.5 0.5 0.338
0.167 0.167 0.446
0.667 0.167 0.446
0.167 0.667 0.446
0.667 0.667 0.446
-0.167 0.333 0.554
0.333 0.333 0.554
-0.167 0.833 0.554
0.333 0.833 0.554
0.0 0.0 0.662
0.5 0.0 0.662
0.0 0.5 0.662
0.5 0.5 0.662
0.0 0.0 0.743"""

response = requests.post(
    "https://atomgpt.org/catalysis/predict",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={
        "substrate": SUBSTRATE,
        "catalyst": CATALYST,
        "adsorbate_indices": [17],
        "model": "default",
    },
)
data = response.json()
if data["success"]:
    sign = "exothermic" if data["adsorption_energy"] < 0 else "endothermic"
    print(f"E_ads = {data['adsorption_energy']} eV ({sign})")
    print(f"  E_sub  = {data['substrate_energy']} eV")
    print(f"  E_cat  = {data['catalyst_energy']} eV")
    print(f"  μ(ads) = {data['chemical_potential']} eV")
    print(f"  Adsorbate: {data['adsorbate_elements']}")

import requests

PT_SUB = open("Pt_substrate.vasp").read()

for ads_file, indices in [("Pt_H.vasp", [5]), ("Pt_O.vasp", [5]), ("Pt_CO.vasp", [5, 6])]:
    cat = open(ads_file).read()
    response = requests.post(
        "https://atomgpt.org/catalysis/predict",
        headers={
            "Authorization": "Bearer sk-XYZ",
            "accept": "application/json",
            "Content-Type": "application/json",
        },
        json={
            "substrate": PT_SUB,
            "catalyst": cat,
            "adsorbate_indices": indices,
        },
    )
    data = response.json()
    if data["success"]:
        ads = "+".join(data["adsorbate_elements"])
        print(f"Pt + {ads}: E_ads = {data['adsorption_energy']:.3f} eV")

AGAPI Agent [WIP]

from agapi.agents import AGAPIAgent
import os

agent = AGAPIAgent(api_key=os.environ.get("AGAPI_KEY"))

# Predict adsorption
response = agent.query_sync("Predict the adsorption energy of O on AgAu surface using ALIGNN-FF")
print(response)

References