Solar Cell Screening¶

Predict theoretical solar cell performance using SLME (spectroscopic limited maximum efficiency) and Shockley-Queisser (SQ) limit. Two modes: (1) input a JARVIS ID to download TBmBJ vasprun.xml and compute SLME + SQ + J-V curves automatically, (2) input custom band gaps and absorption coefficient data for SLME calculation.

Open App

Overview¶

The Solar Cell Screening app uses jarvis.analysis.solarefficiency.solar.SolarEfficiency to compute theoretical photovoltaic performance. For JARVIS materials, it downloads the TBmBJ vasprun.xml from figshare, extracts direct/indirect band gaps and the average absorption coefficient, then sweeps film thickness (1nm–5μm) to find the optimal SLME. It also computes the SQ efficiency limit and full J-V / P-V curves at the optimal thickness.

Data Source

JARVIS TBmBJ — vasprun.xml files from jarvis.db.figshare.data('raw_files')['TBMBJ']. AM1.5G solar spectrum from JARVIS package.

Endpoints¶

`POST /solar/predict-jid` — SLME + SQ from JARVIS ID¶

Downloads the TBmBJ vasprun.xml for the given JARVIS ID, extracts band gaps and absorption coefficient, computes SLME efficiency vs thickness, finds the optimal thickness, and returns SQ efficiency + J-V curves.

curl -X POST "https://atomgpt.org/solar/predict-jid" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-1002"
  }'

curl -X POST "https://atomgpt.org/solar/predict-jid" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-1067"
  }'

Response includes:

Field	Description
`dirgap`	Direct band gap (eV)
`indirgap`	Indirect band gap (eV)
`optimal_thickness`	Optimal film thickness (m)
`optimal_efficiency`	SLME at optimal thickness (%)
`sq_efficiency`	Shockley-Queisser limit (%)
`thickness`	Array of thickness values (m)
`efficiencies`	SLME at each thickness (%)
`V`	Voltage array for J-V curve
`JV`	Current density array (A/m²)
`PV`	Power density array (W/m²)
`optimal_voltage`	Voltage at max power (V)
`J_sc`	Short-circuit current density (A/m²)

`POST /solar/predict-data` — SLME from custom absorption data¶

Compute SLME from user-provided band gaps and absorption coefficient data. Requires at least 5 data points.

curl -X POST "https://atomgpt.org/solar/predict-data" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "indirect_gap": 1.1,
    "direct_gap": 3.4,
    "temperature": 293.15,
    "energies": [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0],
    "absorptions": [0, 0, 100, 5000, 20000, 50000, 80000, 100000, 120000, 150000]
  }'

Field	Type	Default	Description
`indirect_gap`	float	required	Indirect band gap (eV)
`direct_gap`	float	required	Direct band gap (eV)
`temperature`	float	293.15	Temperature (K)
`energies`	list[float]	required	Photon energy array (eV)
`absorptions`	list[float]	required	Absorption coefficient array (cm⁻¹)

Response same as /predict-jid — thickness sweep, optimal SLME, SQ limit, J-V curves.

Python Examples¶

Predict by JIDCustom absorption dataPlot J-V curve

import requests

response = requests.post(
    "https://atomgpt.org/solar/predict-jid",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={"jid": "JVASP-1002"},
)
data = response.json()
if data["success"]:
    print(f"Direct gap: {data['dirgap']:.3f} eV")
    print(f"Indirect gap: {data['indirgap']:.3f} eV")
    print(f"SLME: {data['optimal_efficiency']:.2f}%")
    print(f"SQ limit: {data['sq_efficiency']:.2f}%")
    print(f"Optimal thickness: {data['optimal_thickness']*1e6:.2f} μm")
    print(f"J_sc: {data['J_sc']:.2f} A/m²")

import requests

response = requests.post(
    "https://atomgpt.org/solar/predict-data",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={
        "indirect_gap": 1.1,
        "direct_gap": 3.4,
        "temperature": 300.0,
        "energies": [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0],
        "absorptions": [0, 0, 100, 5000, 20000, 50000, 80000, 100000],
    },
)
data = response.json()
if data["success"]:
    print(f"SLME: {data['optimal_efficiency']:.2f}%")
    print(f"SQ limit: {data['sq_efficiency']:.2f}%")

import requests
import matplotlib.pyplot as plt

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

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
ax1.plot(data["V"], data["JV"])
ax1.set_xlabel("Voltage (V)")
ax1.set_ylabel("Current density (A/m²)")
ax1.set_title("J-V curve")

ax2.plot([t * 1e6 for t in data["thickness"]], data["efficiencies"])
ax2.set_xlabel("Thickness (μm)")
ax2.set_ylabel("SLME (%)")
ax2.set_title(f"SLME vs thickness (opt: {data['optimal_efficiency']:.1f}%)")
plt.tight_layout()
plt.savefig("solar_jvasp1002.png")

AGAPI Agent [WIP]¶

from agapi.agents import AGAPIAgent
import os

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

# Predict by JID
response = agent.query_sync("Compute the solar cell efficiency for JVASP-1002")
print(response)

# Compare materials
response = agent.query_sync("Compare the SLME of JVASP-1002 and JVASP-1067")
print(response)

References¶

K. Choudhary, Comp. Mat. Sci. 259, 114063 (2025) DOI
K. Choudhary, F. Tavazza, Chem. Mater. 31, 5900 (2019) — Accelerated Discovery of Efficient Solar Cell Materials DOI
K. Choudhary et al., Nature Sci. Data 5, 180082 (2018) — JARVIS-DFT DOI
C. Ginter, K. Choudhary, S. Mandal, arXiv:2510.08738 (2025) arXiv
atomgptlab/jarvis