SlakoNet Bands¶

Deep learning tight-binding electronic structure calculator. Three tabs: (1) Band Structure + DOS — high-symmetry path band diagram with atom-projected DOS, (2) 3D Band Surface — interactive kx-ky energy surface with BZ overlay and Fermi plane, (3) Fermi Surface — 2D contour plots, 3D near-Fermi bands, and true 3D Fermi isosurfaces via marching cubes. All powered by SlakoNet neural network Slater-Koster parameters.

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Overview¶

SlakoNet predicts Slater-Koster tight-binding parameters using a deep learning model, enabling rapid electronic band structure and density of states calculations without DFT. The model supports elements up to Z=85 and handles multi-element systems. Max 50 atoms for band structure, max 20 atoms for 3D Fermi isosurface (due to N³ k-mesh cost).

Data Source

SlakoNet pretrained model — neural network Slater-Koster parameters from slakonet.predict_slakonet.

Endpoints¶

`GET /slakonet/bandstructure` — Band structure image (API key)¶

Returns a PNG image of the band structure + DOS along the high-symmetry path. Band gap, VBM, and CBM are in response headers.

curl "https://atomgpt.org/slakonet/bandstructure?jid=JVASP-1002&energy_range_min=-8&energy_range_max=8&APIKEY=sk-XYZ" \
  --output bandstructure_Si.png

With POSCAR string:

curl "https://atomgpt.org/slakonet/bandstructure?poscar=MgB2%0A1.0%0A1.537%20-2.662%200.0%0A1.537%202.662%200.0%0A0.0%200.0%203.515%0AMg%20B%0A1%202%0ACartesian%0A0.0%200.0%200.0%0A1.537%20-0.887%201.757%0A1.537%200.887%201.757&APIKEY=sk-XYZ" \
  --output bandstructure_MgB2.png

Param	Description
`jid`	JARVIS ID — provide `jid` OR `poscar`
`poscar`	URL-encoded POSCAR string
`energy_range_min`	Energy axis min (default -8 eV)
`energy_range_max`	Energy axis max (default 8 eV)
`APIKEY`	API key

Returns image/png with headers: X-Band-Gap, X-VBM, X-CBM, X-Formula.

`POST /slakonet/web_analyze` — Band structure + DOS (JSON)¶

Returns band gap, VBM, CBM, base64 plot, and full band data (eigenvalues, DOS, atom-projected DOS) as JSON.

curl -X POST "https://atomgpt.org/slakonet/web_analyze" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "poscar": "MgB2\n1.0\n1.537 -2.662 0.0\n1.537 2.662 0.0\n0.0 0.0 3.515\nMg B\n1 2\nCartesian\n0.0 0.0 0.0\n1.537 -0.887 1.757\n1.537 0.887 1.757",
    "energy_range_min": -8,
    "energy_range_max": 8
  }'

Response includes band_gap, vbm, cbm, plot_base64 (PNG), formula, num_atoms, and band_data (eigenvalues, dos_energies, dos_values, atom_pdos).

`POST /slakonet/bandstructure3d` — 3D band surface (kx-ky plane)¶

Compute band energies on a 2D Cartesian k-mesh at kz=0 for interactive 3D Plotly surface plots.

curl -X POST "https://atomgpt.org/slakonet/bandstructure3d" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "poscar": "MgB2\n1.0\n1.537 -2.662 0.0\n1.537 2.662 0.0\n0.0 0.0 3.515\nMg B\n1 2\nCartesian\n0.0 0.0 0.0\n1.537 -0.887 1.757\n1.537 0.887 1.757",
    "nk_per_dim": 30
  }'

Field	Type	Default	Description
`poscar`	string	required	POSCAR (max 50 atoms)
`nk_per_dim`	int	30	k-points per axis (10–60)

Response includes kx_grid, ky_grid, bands (one 2D array per band), bz_x/bz_y (BZ outline), bandgap, nbands.

`POST /slakonet/fermisurface` — 2D Fermi surface¶

Compute 2D Fermi contours at kz=0. Returns band energies on k-mesh, Fermi-crossing band indices, BZ outline, and high-symmetry point coordinates (Γ, K, K', M).

curl -X POST "https://atomgpt.org/slakonet/fermisurface" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "poscar": "MgB2\n1.0\n1.537 -2.662 0.0\n1.537 2.662 0.0\n0.0 0.0 3.515\nMg B\n1 2\nCartesian\n0.0 0.0 0.0\n1.537 -0.887 1.757\n1.537 0.887 1.757",
    "nk_per_dim": 40,
    "energy_window": 0.5
  }'

Field	Type	Default	Description
`poscar`	string	required	POSCAR (max 50 atoms)
`nk_per_dim`	int	40	k-points per axis (10–60)
`energy_window`	float	0.5	Energy window around E_F for identifying Fermi bands (eV)

Response includes fermi_bands (indices), band_info (per-band min/max/crosses_ef), kx_1d/ky_1d/kx_grid/ky_grid, bands, high_sym (Γ/K/K'/M coordinates).

`POST /slakonet/fermisurface3d` — 3D Fermi isosurface (marching cubes)¶

Compute a true 3D Fermi isosurface on a full kx-ky-kz mesh using marching cubes (skimage.measure.marching_cubes). Returns mesh vertices and faces for Plotly Mesh3d rendering. Computationally heavy — max 20 atoms, max 35³ k-points.

curl -X POST "https://atomgpt.org/slakonet/fermisurface3d" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "poscar": "MgB2\n1.0\n1.537 -2.662 0.0\n1.537 2.662 0.0\n0.0 0.0 3.515\nMg B\n1 2\nCartesian\n0.0 0.0 0.0\n1.537 -0.887 1.757\n1.537 0.887 1.757",
    "nk_per_dim": 20,
    "energy_window": 0.5
  }'

Field	Type	Default	Description
`poscar`	string	required	POSCAR (max 20 atoms)
`nk_per_dim`	int	20	k-points per axis (8–35, i.e. max 35³ = 42,875 k-points)
`energy_window`	float	0.5	Energy window for Fermi band identification (eV)

Response includes meshes (per-band: vertices_x/y/z, faces_i/j/k for Mesh3d), fermi_bands, band_info, bz_x/bz_y.

Python Examples¶

Band structure by JIDWeb analyze (JSON)3D band surfaceFermi surface

import requests

response = requests.get(
    "https://atomgpt.org/slakonet/bandstructure",
    headers={"accept": "image/png"},
    params={
        "jid": "JVASP-1002",
        "energy_range_min": -8,
        "energy_range_max": 8,
        "APIKEY": "sk-XYZ",
    },
)
with open("bandstructure_Si.png", "wb") as f:
    f.write(response.content)
print(f"Band gap: {response.headers.get('X-Band-Gap')} eV")

import requests

response = requests.post(
    "https://atomgpt.org/slakonet/web_analyze",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "Content-Type": "application/json",
    },
    json={"poscar": open("POSCAR").read()},
)
data = response.json()
print(f"Band gap: {data['band_gap']:.3f} eV")
print(f"VBM: {data['vbm']:.3f}, CBM: {data['cbm']:.3f} eV")

# Save plot
import base64
with open("bands.png", "wb") as f:
    f.write(base64.b64decode(data["plot_base64"]))

import requests

response = requests.post(
    "https://atomgpt.org/slakonet/bandstructure3d",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "Content-Type": "application/json",
    },
    json={"poscar": open("POSCAR").read(), "nk_per_dim": 30},
)
data = response.json()
print(f"Bands: {data['nbands']}, Gap: {data['bandgap']:.3f} eV")
print(f"K-mesh: {data['nk']}×{data['nky']}")

import requests

response = requests.post(
    "https://atomgpt.org/slakonet/fermisurface",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "Content-Type": "application/json",
    },
    json={
        "poscar": open("MgB2.vasp").read(),
        "nk_per_dim": 40,
        "energy_window": 0.5,
    },
)
data = response.json()
print(f"Fermi bands: {data['fermi_bands']}")
print(f"Band gap: {data['bandgap']:.3f} eV")
hs = data["high_sym"]
print(f"Γ={hs['Gamma']}, K={hs['K']}, M={hs['M']}")

AGAPI Agent¶

from agapi.agents import AGAPIAgent
import os

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

# Band structure
response = agent.query_sync("Compute the SlakoNet band structure for MgB2")
print(response)

# Fermi surface
response = agent.query_sync("Show the Fermi surface of MgB2 using SlakoNet")
print(response)

References¶

K. Choudhary, J. Phys. Chem. Lett. 16, 11109 (2025) — SlakoNet DOI
K. Choudhary, Comp. Mat. Sci. 259, 114063 (2025) DOI
atomgptlab/slakonet