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Periodic Table

Interactive periodic table with JARVIS property overlays. The backend aggregates per-element statistics from all 76K+ materials in dft_3d, computing count, mean, min, max, and median for 40+ properties including band gaps, formation energy, mechanical moduli, dielectric constants, thermoelectric coefficients, and more.

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Overview

Data Source

JARVIS dft_3d — per-element statistics aggregated from 76K+ materials.

Endpoints

GET /periodic_table/stats — Per-element aggregated statistics

curl "https://atomgpt.org/periodic_table/stats" \
  -H "Authorization: Bearer sk-XYZ"

Response: Per-element dict: {count, bandgap: {n, mean, min, max, median}, formation_energy: {...}, bulk_modulus: {...}, ...} for 40+ properties.

40+ properties aggregated: bandgap, mbj_bandgap, hse_gap, spillage, formation_energy, ehull, exfoliation_energy, magmom, bulk_modulus, shear_modulus, poisson, epsx/y/z, mepsx/y/z, piezo_eij/dij, max_ir_mode, n/p-Seebeck, slme, max_efg, avg_elec/hole_mass, Tc_supercon, density, and more.

Python Examples

import requests

response = requests.get(
    "https://atomgpt.org/periodic_table/data/JVASP-1002",
    headers={"Authorization": "Bearer sk-XYZ"},
)
data = response.json()
si = data.get("Si", {})
print(f"Si: {si['count']} materials in JARVIS")
bg = si.get("bandgap")
if bg:
    print(f"  Band gap: mean={bg['mean']}, range=[{bg['min']}, {bg['max']}] eV")

AGAPI Agent

from agapi.agents import AGAPIAgent
import os
agent = AGAPIAgent(api_key=os.environ.get("AGAPI_KEY"))
response = agent.query_sync("Show periodic table data")
print(response)

References