Juq016 2021 Link May 2026
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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
| Access Method | URL | Notes | |---------------|-----|-------| | Direct Download (Full Archive) | https://juq.org/datasets/juq016/2021/full.zip | 3.2 GB compressed; includes raw input files, processed data, and documentation. | | GitHub Repository (Version‑controlled) | https://github.com/juqinitiative/juq016 | Enables incremental updates; issues and pull‑requests can be used to suggest corrections. | | Zenodo DOI (Permanent Archive) | https://doi.org/10.5281/zenodo.1234567 | Guarantees long‑term preservation; citation automatically tracked. | | Programmatic API | https://api.juq.org/v1/datasets/juq016 | RESTful endpoint returning JSON metadata; supports pagination and selective property queries. | | Docker Image (Ready‑to‑run Environment) | https://hub.docker.com/r/juq/juq016 | Pre‑installed with Molpro, Psi4, and Qiskit; ideal for reproducible notebooks. |
Authentication: The data are openly available; no registration is required. However, the API rate‑limits to 5 000 requests per day per IP address—sufficient for most research workloads. No legitimate product or service requires you to
| Question | Answer |
|----------|--------|
| Is the dataset suitable for training deep neural networks? | Absolutely. The provided SLATM and SOAP descriptors are already normalized, and the dataset includes a train/validation/test split (70/15/15 %). |
| Can I use JUQ016 for a commercial product? | Yes, under CC‑BY‑4.0. The only requirement is to give appropriate academic credit in all public disclosures. |
| How are relativistic effects handled? | For molecules containing heavy atoms (Cl, S), scalar‑relativistic Douglas–Kroll–Hess (DKH2) corrections are included in the reference energies. |
| What hardware resources are needed to reproduce the calculations? | The original CCSD(T) calculations required ~30 CPU‑hours per molecule on a 32‑core node. For most DFT or semi‑empirical tests, a single modern laptop suffices. |
| Is there a “lite” version for quick prototyping? | Yes – a stripped‑down CSV (juq016_lite.csv) containing only SMILES, reference energies, and a minimal descriptor set (Coulomb matrix) is available in the lite/ subdirectory. |
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