Catalysis Hub GraphQL API Guide

SkillDev tools

Query computational catalysis reaction data via Catalysis Hub GraphQL

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Catalysis Hub GraphQL API Guide skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/domains/chemistry/catalysis-hub-api/SKILL.md and read by ahel’s review.

Overview

Catalysis Hub is an open-access database of DFT-calculated reaction energies and activation barriers for heterogeneous catalysis, developed at SUNCAT Center (Stanford/SLAC). It aggregates computational results from published studies, enabling researchers to search, compare, and reuse DFT data for catalyst screening and mechanism validation.

The GraphQL endpoint provides structured access to reactions, publications, and atomic structures. All data is linked to peer-reviewed publications and includes computational details (DFT code, XC functional, surface facet, coverage).

Authentication

No authentication required. Catalysis Hub is a free public service with no API keys.

GraphQL Schema

Endpoint: https://api.catalysis-hub.org/graphql

All queries use HTTP POST with a JSON query field. Responses follow the Relay connection pattern (edges/node).

Root Query Types

QueryDescription
reactionsDFT-computed reaction energies and barriers
publicationsPublished studies linked to reaction data
systemsAtomic structure data (ASE Atoms objects)
speciesChemical species involved in reactions

Reaction Fields

chemicalComposition, surfaceComposition, facet, reactionEnergy (eV), activationEnergy (eV), dftCode (e.g. Quantum-Espresso, VASP-5.4.4), dftFunctional (e.g. RPBE), reactants (JSON), products (JSON), Equation (e.g. 0.5O2(g) + * -> O*)

Publication Fields

title, authors (JSON), journal, year (Int), doi, reactions (linked Reaction list)

Core Queries

List Reactions

curl -s -X POST "https://api.catalysis-hub.org/graphql" \
  -H "Content-Type: application/json" -d '{"query":"{ reactions(first: 3) { edges { node { chemicalComposition reactionEnergy activationEnergy surfaceComposition } } } }"}'

Response (truncated):

{"data":{"reactions":{"edges":[
  {"node":{"chemicalComposition":"Nb9Sn3","reactionEnergy":-9.687,"activationEnergy":null,"surfaceComposition":"Nb3Sn"}},
  {"node":{"chemicalComposition":"Ir3V9","reactionEnergy":-8.395,"activationEnergy":null,"surfaceComposition":"V3Ir"}},
  {"node":{"chemicalComposition":"Ir9Ni3","reactionEnergy":-2.005,"activationEnergy":null,"surfaceComposition":"Ir3Ni"}}
]}}}

Filter by Surface Composition

curl -s -X POST "https://api.catalysis-hub.org/graphql" \
  -H "Content-Type: application/json" -d '{"query":"{ reactions(first: 2, surfaceComposition: \"Pt\") { edges { node { chemicalComposition surfaceComposition facet reactionEnergy dftCode dftFunctional Equation } } } }"}'

Response (truncated):

{"data":{"reactions":{"edges":[
  {"node":{"chemicalComposition":"Pt28","surfaceComposition":"Pt","facet":"100","reactionEnergy":0.856,"dftCode":"Quantum-Espresso","dftFunctional":"RPBE","Equation":"0.5N2(g) + * -> N*"}},
  {"node":{"chemicalComposition":"Pt28","surfaceComposition":"Pt","facet":"100","reactionEnergy":-0.984,"dftCode":"Quantum-Espresso","dftFunctional":"RPBE","Equation":"0.5O2(g) + * -> O*"}}
]}}}

Search by Chemical Composition (partial match with ~ prefix)

curl -s -X POST "https://api.catalysis-hub.org/graphql" \
  -H "Content-Type: application/json" -d '{"query":"{ reactions(first: 3, chemicalComposition: \"~CO\") { edges { node { chemicalComposition reactionEnergy dftCode } } } }"}'

Response (truncated):

{"data":{"reactions":{"edges":[
  {"node":{"chemicalComposition":"Co9Cr2FeMnNiO20","reactionEnergy":1.910,"dftCode":"VASP-5.4.4"}},
  {"node":{"chemicalComposition":"Co9Cr2FeMnNiO20","reactionEnergy":0.648,"dftCode":"VASP-5.4.4"}},
  {"node":{"chemicalComposition":"Co10CrFeMnNiO20","reactionEnergy":3.167,"dftCode":"VASP-5.4.4"}}
]}}}

Query Publications

curl -s -X POST "https://api.catalysis-hub.org/graphql" \
  -H "Content-Type: application/json" -d '{"query":"{ publications(first: 2, year: 2019) { edges { node { title authors journal year doi } } } }"}'

Response (truncated):

{"data":{"publications":{"edges":[
  {"node":{"title":"High-Throughput Calculations of Catalytic Properties of Bimetallic Alloy Surfaces","authors":"[\"Mamun, Osman\",\"Winther, Kirsten T.\",\"Boes, Jacob R.\",\"Bligaard, Thomas\"]","journal":"Scientific Data","year":2019,"doi":"10.1038/s41597-019-0080-z"}},
  {"node":{"title":"Selective high-temperature CO2 electrolysis enabled by oxidized carbon intermediates","journal":"Nature Energy","year":2019,"doi":"10.1038/s41560-019-0457-4"}}
]}}}

Rate Limits

  • No documented rate limits; add 200-500ms delays between requests as courtesy
  • Use first to limit results; pagination via cursor-based after argument

Academic Use Cases

  • Catalyst Screening: Compare adsorption energies across bimetallic alloy surfaces to identify candidates for target reactions (ORR, NRR, HER)
  • DFT Validation: Cross-reference your DFT results against published values matched by surface, facet, and functional
  • Scaling Relations: Retrieve adsorption energies across surfaces to build Bronsted-Evans-Polanyi (BEP) relations
  • Literature Discovery: Find publications by year or linked reactions for citation and methodology verification

Python Example

import requests

ENDPOINT = "https://api.catalysis-hub.org/graphql"

def query_catalysis_hub(query):
    """Execute a GraphQL query against Catalysis Hub."""
    resp = requests.post(ENDPOINT, json={"query": query})
    resp.raise_for_status()
    return resp.json()["data"]

# Screen adsorption energies on Pt surfaces
data = query_catalysis_hub("""
{
  reactions(first: 20, surfaceComposition: "Pt") {
    edges { node { Equation facet reactionEnergy dftFunctional } }
  }
}
""")
for edge in data["reactions"]["edges"]:
    r = edge["node"]
    print(f"{r['Equation']:<30} facet={r['facet']}  E={r['reactionEnergy']:+.3f} eV")

# Publications with linked reactions
pubs = query_catalysis_hub("""
{
  publications(first: 5, year: 2019) {
    edges { node { title doi reactions { surfaceComposition Equation } } }
  }
}
""")
for edge in pubs["publications"]["edges"]:
    pub = edge["node"]
    print(f"{pub['title']} | DOI: {pub['doi']} | {len(pub.get('reactions') or [])} reactions")

References

Signals

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