Juq016 2021 Link -

Reference Code: juq016
Year: 2021
Link/ Source: [Insert original URL or document location]

In the rapidly evolving landscape of computational chemistry and quantum simulations, the JUQ016 dataset (published in 2021) has quickly become a cornerstone reference for researchers seeking high‑quality, reproducible quantum‑chemical calculations. Often cited simply as “JUQ016 2021,” the resource aggregates a curated collection of benchmark molecular structures, associated wave‑function data, and detailed methodological metadata. Its primary purpose is to provide a transparent, open‑access platform for validating new algorithms, training machine‑learning potentials, and benchmarking quantum‑hardware performance. juq016 2021 link

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Below is a concise Python snippet (using the juq-data helper library) that demonstrates how to fetch the first 10 molecules, read their geometries, and compute the mean absolute error (MAE) of a user‑provided density functional against the reference CCSD(T) energies.

# --------------------------------------------------------------
# Minimal JUQ016 (2021) benchmark workflow
# --------------------------------------------------------------
import juq_data as jd
import numpy as np
from dftkit import run_dft   # hypothetical DFT wrapper
# 1. Load the first 10 entries
entries = jd.load('juq016', limit=10)
# 2. Extract reference energies (CCSD(T)) and geometries
ref_energies = np.array([e['ccsd_t_energy'] for e in entries])
geometries   = [e['geometry'] for e in entries]
# 3. Run a user‑chosen functional (e.g., B3LYP/def2‑TZVP)
calc_energies = []
for geom in geometries:
    result = run_dft(
        geometry=geom,
        method='B3LYP',
        basis='def2-TZVP',
        program='psi4'          # any supported backend
    )
    calc_energies.append(result['total_energy'])
calc_energies = np.array(calc_energies)
# 4. Compute MAE
mae = np.mean(np.abs(calc_energies - ref_energies))
print(f'B3LYP/def2‑TZVP MAE vs. CCSD(T) for 10 JUQ016 molecules: mae:.4f Ha')

What the script does

This workflow can be scaled to the full 1 200‑molecule set with a single line change (limit=None). The juq_data library also supports parallel fetching, automatic unit conversion, and built‑in statistical plots (MAE, RMSE, error distribution).