Wannier TB Bands¶

Compute electronic band structures and density of states from Wannier tight-binding Hamiltonians in the JARVIS-WTB database. Select a material by JARVIS ID, provide a VASP-format k-point path, and the app downloads the wannier90_hr.dat from figshare, diagonalizes the Hamiltonian along the k-path using jarvis.io.wannier.outputs.WannierHam, and returns eigenvalues + DOS.

Open App

Overview¶

The JARVIS-WTB database contains Wannier90 tight-binding Hamiltonians for thousands of materials, enabling rapid band structure calculations without re-running DFT. The app downloads the compressed wannier90_hr.dat file, parses it with WannierHam, computes eigenvalues along a user-specified k-path, and calculates DOS with Gaussian broadening (σ=0.1, 1000 energy points). Results include Wannier function count, Fermi energy, and DFT-vs-Wannier interpolation errors (maxdiff_bz, maxdiff_mesh).

Data Source

JARVIS-WTB — wannier90_hr.dat files from jarvis.db.figshare.data('raw_files')['WANN']. Material metadata from all_wanns.json (formula, space group, Fermi energy, interpolation errors).

Endpoints¶

`GET /wtb/options` — List available WTB materials¶

Returns a dropdown list of all materials in the JARVIS-WTB database with JID, formula, and space group.

curl "https://atomgpt.org/wtb/options" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "accept: application/json"

Response: {"success": true, "options": [{"jid": "JVASP-1002", "formula": "Si", "spg": "Fd-3m"}, ...]}

`POST /wtb/predict` — Compute bandstructure + DOS¶

Select a material by JID and provide a VASP line-mode KPOINTS path. Downloads the Wannier Hamiltonian and computes eigenvalues + DOS.

curl -X POST "https://atomgpt.org/wtb/predict" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-1002",
    "kpoints": "Line mode\n40\nReciprocal\n0.5 0.5 0.5 L\n0.0 0.0 0.0 \\Gamma\n\n0.0 0.0 0.0 \\Gamma\n0.5 0.0 0.5 X\n\n0.5 0.0 0.5 X\n0.5 0.25 0.75 W\n\n0.5 0.25 0.75 W\n0.375 0.375 0.75 K"
  }'

BCC k-path example:

curl -X POST "https://atomgpt.org/wtb/predict" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-816",
    "kpoints": "Line mode\n40\nReciprocal\n0.0 0.0 0.0 \\Gamma\n0.5 -0.5 0.5 H\n\n0.5 -0.5 0.5 H\n0.25 0.25 0.25 P\n\n0.25 0.25 0.25 P\n0.0 0.0 0.0 \\Gamma\n\n0.0 0.0 0.0 \\Gamma\n0.0 0.0 0.5 N"
  }'

Field	Type	Description
`jid`	string	JARVIS ID from the WTB database (use `/wtb/options` to list)
`kpoints`	string	VASP line-mode KPOINTS format (pairs of k-points with labels)

Response:

Field	Description
`bands`	2D array (nbands × nkpts) of eigenvalues (eV, relative to E_F)
`nkpts`	Number of k-points along the path
`kp_labels_points`	k-point indices where labels appear
`kp_labels_text`	High-symmetry point labels (Γ, X, W, K, etc.)
`en_dos`	DOS energy grid (eV)
`dos`	DOS values
`formula`	Chemical formula
`spg`	Space group
`efermi`	Fermi energy (eV)
`nwan`	Number of Wannier functions
`maxdiff_bz`	Max DFT-vs-Wannier error along BZ path (eV)
`maxdiff_mesh`	Max DFT-vs-Wannier error on k-mesh (eV)
`total_time`	Computation time (seconds)
`download_url`	Direct download URL for the wannier90_hr.dat.zip

Python Examples¶

Compute bandstructurePlot bandstructureList available materials

import requests

FCC_KPOINTS = """Line mode
40
Reciprocal
0.5 0.5 0.5 L
0.0 0.0 0.0 \\Gamma

0.0 0.0 0.0 \\Gamma
0.5 0.0 0.5 X

0.5 0.0 0.5 X
0.5 0.25 0.75 W

0.5 0.25 0.75 W
0.375 0.375 0.75 K"""

response = requests.post(
    "https://atomgpt.org/wtb/predict",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={"jid": "JVASP-1002", "kpoints": FCC_KPOINTS},
)
data = response.json()
if data["success"]:
    print(f"{data['formula']} ({data['spg']})")
    print(f"  Wannier functions: {data['nwan']}")
    print(f"  Fermi energy: {data['efermi']:.3f} eV")
    print(f"  Bands: {len(data['bands'])}, K-points: {data['nkpts']}")
    print(f"  Maxdiff BZ: {data['maxdiff_bz']} eV")
    print(f"  Time: {data['total_time']}s")

import requests
import matplotlib.pyplot as plt

response = requests.post(
    "https://atomgpt.org/wtb/predict",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={"jid": "JVASP-1002", "kpoints": open("KPOINTS").read()},
)
data = response.json()

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), gridspec_kw={"width_ratios": [3, 1]})

# Band structure
for band in data["bands"]:
    ax1.plot(range(data["nkpts"]), band, "b-", lw=0.8)
ax1.axhline(0, color="gray", ls="--", lw=0.5)
ax1.set_xticks(data["kp_labels_points"])
ax1.set_xticklabels(data["kp_labels_text"])
ax1.set_ylabel("E - E_F (eV)")
ax1.set_ylim(-4, 4)
ax1.set_title(f"{data['formula']} ({data['spg']})")

# DOS
ax2.plot(data["dos"], data["en_dos"], "b-", lw=1.5)
ax2.fill_betweenx(data["en_dos"], 0, data["dos"], alpha=0.15)
ax2.axhline(0, color="gray", ls="--", lw=0.5)
ax2.set_xlabel("DOS")
ax2.set_ylim(-4, 4)
ax2.set_yticklabels([])

plt.tight_layout()
plt.savefig(f"wtb_{data['jid']}.png", dpi=150)

import requests

response = requests.get(
    "https://atomgpt.org/wtb/options",
    headers={"Authorization": "Bearer sk-XYZ"},
)
data = response.json()
print(f"Total WTB materials: {len(data['options'])}")
for opt in data["options"][:10]:
    print(f"  {opt['jid']:12s} {opt['formula']:10s} {opt['spg']}")

AGAPI Agent¶

from agapi.agents import AGAPIAgent
import os

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

# Compute band structure
response = agent.query_sync("Compute Wannier TB bandstructure for Si (JVASP-1002)")
print(response)

References¶

K. Choudhary et al., Sci. Data 8, 106 (2021) — JARVIS-WTB DOI
K. Choudhary et al., npj Comp. Mat. 6, 173 (2020) DOI
atomgptlab/jarvis