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XRD / DiffractGPT

XRD analysis suite: simulate powder XRD patterns from crystal structures, match experimental data to JARVIS-DFT, Rietveld-style refinement, AI-powered peak identification via DiffractGPT, POSCAR to XYZ conversion.

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Overview

XRD analysis suite: simulate powder XRD patterns from crystal structures, match experimental data to JARVIS-DFT, Rietveld-style refinement, AI-powered peak identification via DiffractGPT, POSCAR to XYZ conversion.

Data Source

dft_3d + DiffractGPT model

Endpoints

  • GET /xrd
  • POST /xrd/query
  • GET /pxrd/query
  • GET /xrd/analyze
  • POST /xrd/analyze
  • POST /xrd/analyze_with_refinement
  • GET /diffractgpt/query
  • POST /xrd/poscar_to_xyz
  • POST /xrd/generate

Request Models: XRDAnalysisRequest, XRDRefinementRequest, XRDGenerateRequest

Authentication

All POST endpoints require Authorization: Bearer YOUR_TOKEN.

API Example

import requests

response = requests.post(
    "https://atomgpt.org/xrd/query",
    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 xrd / diffractgpt for Silicon")
print(response)

Reference

  • J. Phys. Chem. Lett. 16, 2110 (2025)