SlakoNet Bands¶

Deep learning tight-binding band structures from neural network Slater-Koster parameters. Query by JARVIS ID or upload POSCAR. Web analyze endpoint returns band structure + DOS.

Open App

Overview¶

Deep learning tight-binding band structures from neural network Slater-Koster parameters. Query by JARVIS ID or upload POSCAR. Web analyze endpoint returns band structure + DOS.

Data Source

SlakoNet pretrained model

Endpoints¶

GET /slakonet
GET /slakonet/bandstructure
POST /slakonet/bandstructure
POST /slakonet/web_analyze

Request Models: —

Authentication

All POST endpoints require Authorization: Bearer YOUR_TOKEN.

API Example¶

import requests

response = requests.post(
    "https://atomgpt.org/slakonet/bandstructure",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={"jid": "JVASP-1002"},
)
data = response.json()
print(data)

AGAPI Agent¶

from agapi.agents import AGAPIAgent
import os

agent = AGAPIAgent(api_key=os.environ.get("AGAPI_KEY"))
response = agent.query_sync("Show slakonet bands for Silicon")
print(response)

Reference¶

J. Phys. Chem. Lett. 16, 11109 (2025)