Release sage 2.0.0

README.md

@@ -35,6 +35,9 @@ Datasets must be submitted as pull requests.
     conda env create -f qca-dataset-submission/devtools/prod-envs/qcarchive-user-submit.yaml
     conda activate qcarchive-user-submit
     ```
+   You may also need to install OpenEye:\
+   `conda install -c openeye openeye-toolkits`
+
 
 
 4. Choose a starting notebook and README based on the type of dataset you wish to submit:
@@ -202,6 +205,10 @@ The status only refers to the `default` specification which is required for all
 
 [![Running](https://img.shields.io/badge/Status-Running-orange)](https://img.shields.io/badge/Status-Running-orange)  the dataset is currently running and may have some incomplete jobs.
 
+# Forcefield Release Datasets
+| Forcefield  | Repo  | Optimization | Torsion Drive | Elements | Zenodo |
+|-------------|----------|-------------------|--------------------|----------|--------|
+| Release OpenFF 2.0.0 Sage | [openff-sage](https://github.com/openforcefield/openff-sage)  | [2024-12-12-OpenFF-Sage-2.0.0-Training-Optimization-Dataset-v1.0](https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2024-12-12-OpenFF-Sage-2.0.0-Training-Optimization-Dataset-v1.0) | [Coming Soon]() | H, C, N, O, S, P, F, Cl, Br, I  | [Coming Soon]() |
 
 
 # Basic Datasets

submissions/2024-12-12-OpenFF-Sage-2.0.0-Training-Optimization-Dataset-v1.0/README.md

@@ -0,0 +1,69 @@
+# OpenFF Sage 2.0.0 Training Optimization v1.0
+
+### Description
+
+A quantum chemical (QC) dataset curated to train [OpenFF 2.0.0 Sage](https://github.com/openforcefield/openff-sage) forcefield, with reparametrized Lennard-Jones (LJ) and valence parameters, the latter relevant to this dataset. This QC dataset with the OpenFF default level of theory, B3LYP-D3BJ/DZVP, is used to benchmark Sage geometries and energetics. These optimized conformer geometries where used in conjunction with the QC dataset used to train one dimensional torsional profiles. This Generation 2 dataset increases chemical diversity when compared to Generation 1, which are of value to our industry partners. Large molecules (>20 heavy atoms) were also included, including more flexible molecules and a greater degree of conformational variation which provide intramolecular interactions.
+
+This is the complete optimization dataset used for training OpenFF 2.0.0 Sage, consisting of the following datasets:
+
+ - [OpenFF Gen 2 Opt Set 1 Roche](https://github.com/openforcefield/qca-dataset-submission/tree/0e6e6da930118e2a2d6402b93c3e3e93830600cc/submissions/2020-03-20-OpenFF-Gen-2-Optimization-Set-1-Roche)
+ - [OpenFF Gen 2 Opt Set 2 Coverage](https://github.com/openforcefield/qca-dataset-submission/tree/0e6e6da930118e2a2d6402b93c3e3e93830600cc/submissions/2020-03-20-OpenFF-Gen-2-Optimization-Set-2-Coverage)
+ - [OpenFF Gen 2 Opt Set 3 Pfizer Discrepancy](https://github.com/openforcefield/qca-dataset-submission/tree/0e6e6da930118e2a2d6402b93c3e3e93830600cc/submissions/2020-03-20-OpenFF-Gen-2-Optimization-Set-3-Pfizer-Discrepancy)
+ - [OpenFF Gen 2 Opt Set 4 eMolecules  - Discrepancy](https://github.com/openforcefield/qca-dataset-submission/tree/0e6e6da930118e2a2d6402b93c3e3e93830600cc/submissions/2020-03-20-OpenFF-Gen-2-Optimization-Set-4-eMolecules-Discrepancy)
+ - [OpenFF Gen 2 Opt Set 5 Bayer](https://github.com/openforcefield/qca-dataset-submission/tree/0e6e6da930118e2a2d6402b93c3e3e93830600cc/submissions/2020-03-20-OpenFF-Gen-2-Optimization-Set-5-Bayer)
+
+The following filters were applied to those datasets:
+
+ - `RecordStatusFilter(status=RecordStatusEnum.complete)`
+ - `ConnectivityFilter(tolerance=1.2)`
+ - `UndefinedStereoFilter()`
+ - `ElementFilter(allowed_elements=["H", "C", "N", "O", "S", "P", "F", "Cl", "Br", "I"])`
+ - `ConformerRMSDFilter(max_conformers=10)`
+
+ Further information can be found in the curation scripts for the linked repositories.
+
+### General Information
+
+- Date: 2024-12-12
+- Class: OpenFF Optimization Dataset
+- Purpose: Complete set of training data for OpenFF 2.0.0 Sage
+- Dataset Type: optimization
+- Name: OpenFF Sage 2.0.0 Training Optimization Dataset v1.0
+- Number of unique molecules:   1025
+- Number of filtered molecules: 0 
+- Number of conformers:         3663
+- Number of conformers (min mean max): 1.00, 3.53, 10.00
+- Mean molecular weight: 261.38
+- Max molecular weight: 544.64
+- Set of charges: -2.0, -1.0, 0.0, 1.0
+- Dataset Submitter: Jennifer A. Clark
+- Dataset Curator: Simon Boothroyd
+- Dataset Generator: Hyesu Jang
+
+### QCSubmit generation pipeline
+
+- `generate-combined-dataset.py`: A python script which shows how the dataset was prepared from the input files.
+- `output.txt`: A text file containing the printed output of `generate-combined-dataset.py`.
+
+### QCSubmit Manifest
+
+- `generate-combined-dataset.py`
+- `dataset.json.bz2`: The basic dataset ready for submission.
+- `dataset.pdf`: A pdf file containing molecule 2D structures.
+- `dataset.smi`: SMILES for every molecule in the submission.
+
+### Metadata
+
+* Elements: {F, I, N, C, P, Cl, S, Br, O, H}
+* QC Specifications: default
+  * basis: DZVP
+  * implicit_solvent: None
+  * keywords: {}
+  * maxiter: 200
+  * method: B3LYP-D3BJ
+  * program: psi4
+  * SCF Properties:
+    * dipole
+    * quadrupole
+    * wiberg_lowdin_indices
+    * mayer_indices

submissions/2024-12-12-OpenFF-Sage-2.0.0-Training-Optimization-Dataset-v1.0/conda_env.yml

@@ -0,0 +1,161 @@
+name: qcarchive-user-submit
+channels:
+- conda-forge
+- openeye
+dependencies:
+- annotated-types=0.7.0=pyhd8ed1ab_1
+- apsw=3.47.0.0=py311hde754ab_0
+- argcomplete=3.5.2=pyhd8ed1ab_0
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+- basis_set_exchange=0.10=pyhd8ed1ab_1
+- brotli=1.1.0=hd74edd7_2
+- brotli-bin=1.1.0=hd74edd7_2
+- brotli-python=1.1.0=py311h3f08180_2
+- bson=0.5.9=py_0
+- bzip2=1.0.8=h99b78c6_7
+- ca-certificates=2024.8.30=hf0a4a13_0
+- cached-property=1.5.2=hd8ed1ab_1
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+- cairo=1.18.2=h6a3b0d2_1
+- certifi=2024.8.30=pyhd8ed1ab_0
+- cffi=1.17.1=py311h3a79f62_0
+- chardet=5.2.0=py311h267d04e_2
+- charset-normalizer=3.4.0=pyhd8ed1ab_1
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+- font-ttf-inconsolata=3.000=h77eed37_0
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+- font-ttf-ubuntu=0.83=h77eed37_3
+- fontconfig=2.15.0=h1383a14_1
+- fonts-conda-ecosystem=1=0
+- fonts-conda-forge=1=0
+- fonttools=4.55.3=py311h4921393_0
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+- jsonschema=4.23.0=pyhd8ed1ab_1
+- jsonschema-specifications=2024.10.1=pyhd8ed1ab_1
+- kiwisolver=1.4.7=py311h2c37856_0
+- krb5=1.21.3=h237132a_0
+- lcms2=2.16=ha0e7c42_0
+- lerc=4.0.0=h9a09cb3_0
+- libblas=3.9.0=25_osxarm64_openblas
+- libboost=1.84.0=hc9fb7c5_7
+- libboost-python=1.84.0=py311h8fc16d6_7
+- libbrotlicommon=1.1.0=hd74edd7_2
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+- libbrotlienc=1.1.0=hd74edd7_2
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+- libcxx=19.1.5=ha82da77_0
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+- libjpeg-turbo=3.0.0=hb547adb_1
+- liblapack=3.9.0=25_osxarm64_openblas
+- liblzma=5.6.3=h39f12f2_1
+- libopenblas=0.3.28=openmp_hf332438_1
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+- munkres=1.1.4=pyh9f0ad1d_0
+- ncurses=6.5=h7bae524_1
+- networkx=3.4.2=pyh267e887_2
+- numpy=1.26.4=py311h7125741_0
+- openeye-toolkits=2024.2.0=py311_0
+- openff-amber-ff-ports=0.0.4=pyhca7485f_0
+- openff-forcefields=2024.09.0=pyhff2d567_0
+- openff-qcsubmit=0.54.0=pyhd8ed1ab_0
+- openff-toolkit-base=0.16.7=pyhd8ed1ab_0
+- openff-units=0.2.2=pyhca7485f_0
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+- pint=0.23=pyhd8ed1ab_1
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+- pytest=8.3.4=pyhd8ed1ab_1
+- python=3.11.11=hc22306f_1_cpython
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+- python-dateutil=2.9.0.post0=pyhff2d567_1
+- python-tzdata=2024.2=pyhd8ed1ab_1
+- python_abi=3.11=5_cp311
+- pytz=2024.2=pyhd8ed1ab_1
+- pyyaml=6.0.2=py311h460d6c5_1
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+- qcportal=0.56=pyhd8ed1ab_1
+- qhull=2020.2=h420ef59_5
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+- sqlite=3.47.0=hcd14bea_1
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+- tzdata=2024b=hc8b5060_0
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+- zstd=1.5.6=hb46c0d2_0
+