diff --git a/README.md b/README.md
index b9b70a04..685b6e62 100644
--- a/README.md
+++ b/README.md
@@ -252,6 +252,7 @@ These are currently used to find a minimum energy conformation of a molecule.
 | `OpenFF Torsion Benchmark Supplement Optimization Dataset v1.0` | [2024-04-18-OpenFF-Torsion-Benchmark-Supplement-Optimization-Dataset-v1.0](https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2024-04-18-OpenFF-Torsion-Benchmark-Supplement-Optimization-Dataset-v1.0) | Additional optimizations for benchmarking Sage 2.2.0 proper torsions and new parameters from the torsion multiplicity work | H, C, N, O, F, P, S, Cl, Br | |
 | `OpenFF Torsion Multiplicity Optimization Training Coverage Supplement v1.0` | [2024-06-20-OpenFF-Torsion-Multiplicity-Optimization-Training-Coverage-Supplement-v1.0](https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2024-06-20-OpenFF-Torsion-Multiplicity-Optimization-Training-Coverage-Supplement-v1.0) | Additional optimization training data for Sage 2.2.0 proper torsions and new parameters from the torsion multiplicity work | C, Cl, S, O, H, P, N, Br | |
 | `OpenFF Torsion Multiplicity Optimization Benchmarking Coverage Supplement v1.0` | [2024-06-24-OpenFF-Torsion-Multiplicity-Optimization-Benchmarking-Coverage-Supplement-v1.0](https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2024-06-24-OpenFF-Torsion-Multiplicity-Optimization-Benchmarking-Coverage-Supplement-v1.0) | Additional optimization benchmarking data for Sage 2.2.0 proper torsions and new parameters from the torsion multiplicity work | Cl, H, I, S, O, N, Br, C, P | |
+|`OpenFF Iodine Fragment Opt v1.0` | [2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0](https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0) | B3LYP-D3BJ/DZVP optimized conformers for a variety of I-containing fragment molecules | C, O, I, S, F, Br, Cl, N, H ||
 
 
 # TorsionDrive Datasets
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/README.md b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/README.md
new file mode 100644
index 00000000..1ba75c86
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/README.md
@@ -0,0 +1,60 @@
+# OpenFF Iodine Fragment Opt v1.0
+
+## Description
+
+A dataset containing fragments of molecules from the Zinc, Enamine 10240, Enamine 50240, and CHEMBL datasets, optimized at the B3LYP-D3BJ/DZVP level of theory. Molecules containing I were fragmented into small fragments. 
+Fragments containing I then had protomers enumerated.
+
+For each resulting molecule, a set of up to 5 conformers were generated by:
+  * generating a set of up to 1000 conformers with a RMS cutoff of 0.5 Å using the OpenEye backend of the OpenFF toolkit
+  * applying ELF conformer selection (max 5 conformers) using OpenEye
+
+
+## General information
+
+* Date: 2024-09-10
+* Class: OpenFF OptimizationDataset
+* Purpose: Geometry optimization for eventual ESP calculation
+* Name: OpenFF Iodine Fragment Opt v1.0
+* Number of unique molecules: 526
+* Number of conformers: 531
+* Number of conformers per molecule (min, mean, max): 1, 1.01, 5
+* Molecular weight (min, mean, max): (155.97, 242.52, 316.94)
+* Charges: [-1.0, 0.0, 1.0, 2.0]
+* Dataset submitter: Alexandra McIsaac
+* Dataset generator: Alexandra McIsaac
+
+
+## QCSubmit generation pipeline
+
+* `generate-dataset.ipynb`: This notebook shows how the dataset was prepared from the input files.
+    * Conformers were generated and selected using OpenEye
+
+
+## QCSubmit Manifest
+
+* `dataset.json.bz2`: Compressed dataset ready for submission
+* `dataset.pdf`: Visualization of dataset molecules
+* `dataset.smi`: Smiles strings for dataset molecules
+* `generate-dataset.ipynb`: Notebook describing dataset generation and submission
+* `enumerated.smi`: Input SMILES
+* `input-environment.yaml`: Environment file used to create Python environment for the notebook
+* `full-environment.yaml`: Fully-resolved environment used to execute the notebook.
+
+
+## Metadata
+
+* Elements: {C, I, Br, Cl, S, N, H, F, O}
+* unique molecules: 526
+* Spec: default
+	* basis: DZVP
+	* implicit_solvent: None
+	* keywords: {}
+	* maxiter: 200
+	* method: B3LYP-D3BJ
+	* program: psi4
+	* SCF properties:
+		* dipole
+		* quadrupole
+		* wiberg_lowdin_indices
+		* mayer_indices
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.json.bz2 b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.json.bz2
new file mode 100644
index 00000000..20bb7a62
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.json.bz2
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d324e5dbf3865fbe68e37c2c314ffe423b4c25c2669828bbd0243713920b8e1a
+size 134075
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.pdf b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.pdf
new file mode 100644
index 00000000..4d28246a
Binary files /dev/null and b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.pdf differ
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.smi b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.smi
new file mode 100644
index 00000000..b0d142a7
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/dataset.smi
@@ -0,0 +1,526 @@
+CCI
+CC=CI
+C(C=O)I
+CC#CI
+C#CCI
+C(=O)C#CI
+COCCI
+C(C#CO)I
+C1=COC(=N1)I
+C1=C(N=NN1)I
+C1=NNN=C1I
+C1=CON=C1I
+C1=CN=C(N1)I
+C1=C(C=NN1)I
+C1(=NN=NN1)I
+C1(=NN=N[N-]1)I
+C1=C(C=NO1)I
+C1=C(N=CO1)I
+C1=NC(=NN1)I
+C1=NNC(=N1)I
+C1(=NNN=N1)I
+C1=CNN=C1I
+C1=C(NN=C1)I
+C(C(C=O)N)I
+C(C(C=O)[NH3+])I
+C1=NN=C(N1)I
+C1=COC(=C1)I
+C1=C(N=CN1)I
+C1=C(NC=N1)I
+C1=C(SC=N1)I
+CCOCCI
+C1=C(NN=N1)I
+C(C(F)(F)F)(F)I
+CC1=CC(=CN1)I
+c1ccnc(c1)I
+CC1=NOC=C1I
+C1=NNC(=C1I)N
+C1=C(C(=NN1)N)I
+c1cncnc1I
+c1cnc(cn1)I
+c1cnccc1I
+c1ccc(cc1)I
+C1=NNN=C1CI
+C1=C(N=NN1)CI
+CC1=CC(=NN1)I
+CC1=NNC(=C1)I
+c1c(cncn1)I
+C1CC=NC(=C1)I
+C1CC(=NC=C1)I
+C(C(F)(F)I)(F)F
+CC1=C(C=NO1)I
+C1=C(SC(=N1)N)I
+C1=C(C(=CS1)I)N
+CC1=C(C=NN1)I
+CC1=NNC=C1I
+CC1=CC=C(O1)I
+c1cc(cnc1)I
+c1cc(c[nH+]c1)I
+C1C=CC=CC1I
+CN1C=NC=C1I
+CC1=C(N=NN1)I
+CC1=NNN=C1I
+CC1(CCNCC1)I
+CC1(CC[NH2+]CC1)I
+Cc1c(cccn1)I
+c1cnc(cc1Br)I
+Cc1ccc(cc1)I
+Cc1cnccc1I
+C1=NC(=C(S1)I)C=O
+c1cc(nc(c1)I)N
+C(=O)C1=NNN=C1I
+C(=O)C1=C(NN=N1)I
+c1c(cncc1I)Cl
+c1cc(c(nc1)I)Cl
+c1c[nH+]cc(c1N)I
+c1cncc(c1N)I
+Cc1ccc(cn1)I
+CC1=C(C(=NN1)N)I
+CC1=NNC(=C1I)N
+Cc1ccccc1I
+c1cc(ncc1I)F
+c1cc(c(nc1)I)N
+c1cnc(cc1I)Br
+C1=NNC(=C1I)C#N
+C1=C(C(=NN1)C#N)I
+Cc1ccnc(c1)I
+c1ccc(c(c1)F)I
+c1c[nH+]c(cc1N)I
+c1cnc(cc1N)I
+CC1=CC(=C(S1)C)I
+c1cncc(c1Cl)I
+c1cncc(c1I)Cl
+C1COC(C1I)CO
+CCC[N+](C)(C)CI
+c1c(cncc1I)Br
+C(=O)C1=NNC(=N1)I
+C(=O)C1=NC(=NN1)I
+Cc1cc(cnc1)I
+c1cc(c(nc1)I)F
+c1cc(c(nc1)I)Br
+c1cc(c(nc1)F)I
+Cc1ccncc1I
+c1cnc(cc1Cl)I
+c1cc(c(nc1)I)O
+c1cc(cc(c1)I)O
+c1c(cnc(n1)N)I
+C1=C(SC=C1C=O)I
+c1cnc(cc1F)I
+c1ccc(c(c1)N)I
+c1cnc(cc1I)F
+c1cc(ncc1I)N
+c1cc(c(nc1)Br)I
+CC1=C(SC(=N1)N)I
+c1cncc(c1I)Br
+c1cncc(c1Br)I
+c1cnc(nc1I)N
+c1cc(nc(c1)I)F
+c1cc(ccc1N)I
+c1cnc(cc1I)N
+C1=C(C=NNC1=O)I
+c1c(cncc1I)N
+c1c(cncc1I)F
+C1=CSC(=C1I)C=O
+C1C=CC=CC1(O)I
+C1=C(C(=NN1)I)C=O
+C1=NNC(=C1C=O)I
+C1=C(C(=NN1)C=O)I
+C1=NNC(=C1I)C=O
+c1cc(ccc1O)I
+c1cc(c(nc1)N)I
+CC1=C(C(=NN1)C)I
+C1=NOC(=C1I)C#N
+c1c(ncc(n1)I)Cl
+c1cc(ncc1Br)I
+c1cncc(c1F)I
+Cc1cccnc1I
+C1=C([NH+]=C(N1)C=O)I
+C1=C(N=C(N1)C=O)I
+c1cc(ncc1I)Cl
+c1ccc(c(c1)O)I
+C1C(CNC1=O)CI
+c1cc(cc(c1)I)N
+c1cc(cc(c1)I)F
+c1c(c[nH+]cc1I)N
+CC1=C(NC(=N1)C)I
+CC1=C(N=C(N1)C)I
+C1=C(C=NC(=O)N1)I
+c1cnc(cc1I)Cl
+C1=C(OC(=C1)I)C=O
+c1cncc(c1I)N
+Cc1cccc(n1)I
+C1=C(N=C(O1)I)C=O
+CNc1ccc(cn1)I
+CSc1cc(cnc1)I
+COc1cnccc1I
+CSc1ccnc(c1)I
+c1cc(ccc1C=O)I
+c1cncc(c1C[NH3+])I
+CCc1cccnc1I
+C1(=O)C(=NNC(=O)N1)I
+c1cc(nc(c1)I)C[NH3+]
+Cc1ccc(cc1I)N
+C1=NNC(=C1I)C(=O)[O-]
+C1=C(C(=NN1)C(=O)[O-])I
+COc1cc(cnc1)I
+c1cc(ncc1I)C[NH3+]
+c1cc(cc(c1)I)C#N
+c1c(cncc1I)C[NH3+]
+CC(C[N+](C)(C)CI)O
+c1ccc(c(c1)C[NH3+])I
+CNc1cc(ccn1)I
+C1=C(C(=O)NC(=N1)N)I
+c1c(c(nc(n1)N)[O-])I
+CN1C=C(C=C1C=O)I
+c1cc(c(nc1)I)C[NH3+]
+c1cc(cc(c1)I)C=O
+CC1=C(OC(=N1)I)C=O
+C1=C(C(=O)NC(=O)N1)I
+CNc1cc(cnc1)I
+CNc1ccccc1I
+c1cc(cc(c1)I)C[NH3+]
+CC1=CC(=O)C(=CN1)I
+C1=CC(=O)C(=CC1=O)I
+c1cncc(c1C#N)I
+c1c(cncc1I)C=O
+c1cc(c([nH+]c1)I)CN
+C(=O)C1=C(SC(=N1)Cl)I
+c1cncc(c1I)C[NH3+]
+c1cnc(cc1I)C[NH3+]
+c1c[nH+]c(c(c1N)O)I
+c1cnc(c(c1N)[O-])I
+CCc1ccnc(c1)I
+C1=C(SC(=C1)I)S(=O)[O-]
+c1c(cnc(c1O)I)Br
+C1=C(C(=C(S1)I)Br)C=O
+c1cnc(cc1I)C#N
+CN1C=C(C(=N1)C=O)I
+c1cc(c(cc1N)I)Br
+c1cnc(cc1C[NH3+])I
+c1cncc(c1C=O)I
+COc1cccnc1I
+C1=NC(=C(C(=O)N1)I)N
+C1=C(SC(=C1)I)CCN
+C1=C(SC(=C1)I)CC[NH3+]
+C1=C(C(=NC(=O)N1)O)I
+C1=C(C(=O)C(=CN1)I)N
+COc1c(cccn1)I
+CC1=C(SC(=N1)I)C=O
+c1cc(c(nc1)C[NH3+])I
+Cc1c(cnc(n1)N)I
+CCc1c(cccn1)I
+CSc1ccc(nc1)I
+c1cc(nc(c1I)N)F
+c1cc(c(nc1)I)C=O
+c1c(cnc(c1I)N)F
+C1CC1C2=NNC=C2I
+C1CC1C2=C(C=NN2)I
+CNc1ccc(nc1)I
+CSc1c(cccn1)I
+CN1C(=CC(=N1)C=O)I
+C1=C(C(=O)NC=C1F)I
+C1=C(SC(=C1N)C=O)I
+CCc1ccc(nc1)I
+CNc1c(cccn1)I
+c1cc(c(c(c1)F)I)O
+C1=C(NC(=O)NC1=O)I
+c1cncc(c1I)C=O
+c1cc(nc(c1)I)C#N
+COc1cccc(c1)I
+CNc1cccnc1I
+c1cc(c(nc1)C#N)I
+CC1=C(C(=O)NN1C)I
+CCc1ccncc1I
+CSc1cc(ccn1)I
+Cc1c(cnc(n1)C)I
+c1ccc(c(c1)C=O)I
+CSc1cccc(n1)I
+C1=C(OC(=C1I)C=O)Br
+CC1=C(C(=NN1C)N)I
+C1=NC(=C(N1)I)CC[NH3+]
+C1=NC(=C(N1)I)CCN
+C1=NC(=C(N1)CC[NH3+])I
+CSc1cccnc1I
+c1cc(c(c(c1)I)O)N
+CNc1cccc(c1)I
+COc1ccnc(c1)I
+C1=C(C(=NN1)C(=O)N)I
+C1=NNC(=C1I)C(=O)N
+COc1cc(ccn1)I
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+CNc1ccnc(c1)I
+c1cncc(c1CN)I
+CNc1cccc(n1)I
+CNc1cccc([nH+]1)I
+c1cnc(cc1C#N)I
+CNc1cnccc1I
+C1=NC(=O)NC(=C1I)N
+C1=C(C(=NC(=O)N1)N)I
+CCc1cc(ccn1)I
+COc1ccccc1I
+c1cncc(c1I)CN
+c1ccc(c(c1)C#N)I
+CC(=O)C1=NC(=CN1)I
+CC(=O)C1=[NH+]C(=CN1)I
+CSc1ccncc1I
+C1=NNC(=O)C(=C1[O-])I
+C1=NNC(=C(C1=O)I)O
+c1cncc(c1I)C#N
+c1cnc(c(n1)C=O)I
+CC1=CSC(=C1I)C=O
+c1cc(ncc1C[NH3+])I
+CCc1cnccc1I
+CN1C(=C(C=N1)I)C=O
+c1cc(ncc1I)C#N
+Cc1c(ccc(n1)N)I
+CC1=CC(=CNC1=O)I
+c1cc(c(cc1O)N)I
+c1c(cc(cc1F)I)N
+c1ccc(c(c1)CO)I
+c1cnc(c(c1N)O)I
+C1=NC(=C(N1)[N+](=O)[O-])I
+C1=C(NN=C1C=O)CI
+C1=C(NN=C1CI)C=O
+c1c(c(ncn1)C=O)I
+c1cnc(cc1I)C(=O)[O-]
+CCc1cccc(c1I)C
+c1cc(c(c(c1)O)I)C=O
+CC(C)c1ccnc(c1)I
+c1cc(c(cc1F)I)C=O
+CC1=CC(=CN(C1=O)C)I
+c1ccc2c(c1)NC(=N2)I
+CN1C(=C(C(=N1)N)I)C=O
+c1cc(c(c(c1N)I)F)F
+CNCc1ccccc1I
+C[NH2+]Cc1ccccc1I
+Cc1cc(c(c(c1)I)N)C
+CC1=C(C(=NN1C)C=O)I
+CN(C)c1ccccc1I
+c1cc(c(c(c1)I)C=O)O
+c1cnc2c(c1I)C=NN2
+c1cnc2=NNC=c2c1I
+c1cc(c(c(c1)I)C=O)F
+Cc1cccc(c1I)C=O
+c1c(cnc(c1N)I)C=O
+CN1C(=O)C(=CN=C1N)I
+CC(C)c1ccncc1I
+CN(C)c1ccncc1I
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+CC1=C(NC(=C1C)I)C=O
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+c1cnc(c(c1I)C=O)F
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+CN(C)c1c(cccn1)I
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diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/enumerated.smi b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/enumerated.smi
new file mode 100644
index 00000000..41a0c7c0
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/enumerated.smi
@@ -0,0 +1,526 @@
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+[H]c1c(nc(c(c1I)[H])N([H])C([H])([H])[H])[H]
+[H]C1=C(C(=O)N(C(=N1)N([H])[H])[H])I
+[H]c1nc(N([H])[H])nc([O-])c1I
+[H]C(=O)c1c([H])c(I)c([H])n1C([H])([H])[H]
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+[H]c1c(c(c(c(c1[H])I)[H])C(=O)[H])[H]
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+[H]c1c(I)c(=O)n([H])c(=O)n1[H]
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+[H]c1c(c(c(c(c1[H])N([H])C([H])([H])[H])I)[H])[H]
+[H]c1c(c(c(c(c1[H])I)[H])C([H])([H])[N+]([H])([H])[H])[H]
+[H]c1c(C([H])([H])[H])n([H])c([H])c(I)c1=O
+[H]C1=C(C(=O)C(=C(C1=O)[H])I)[H]
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+[H]c1c(I)sc(S(=O)[O-])c1[H]
+[H]c1c(c(nc(c1Br)[H])I)O[H]
+[H]C1=C(C(=C(S1)I)Br)C(=O)[H]
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+[H]C1=C(C(=NN1C([H])([H])[H])C(=O)[H])I
+[H]c1c([H])c(N([H])[H])c([H])c(I)c1Br
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+[H]c1c(nc(c(c1C(=O)[H])I)[H])[H]
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+[H]c1c(I)sc(C([H])([H])C([H])([H])N([H])[H])c1[H]
+[H]C1=C(SC(=C1[H])I)C([H])([H])C([H])([H])[N+]([H])([H])[H]
+[H]Oc1nc(=O)n([H])c([H])c1I
+[H]C1=C(C(=O)C(=C(N1[H])[H])I)N([H])[H]
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+[H]C1=NN(C(=C1I)C2(C(C2([H])[H])([H])[H])[H])[H]
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+[H]c1c(c(c(nc1[H])SC([H])([H])[H])I)[H]
+[H]C1=C(N(N=C1C(=O)[H])C([H])([H])[H])I
+[H]C1=C(C(=O)N(C(=C1F)[H])[H])I
+[H]C1=C(SC(=C1N([H])[H])C(=O)[H])I
+[H]c1c(c(nc(c1C([H])([H])C([H])([H])[H])[H])I)[H]
+[H]c1c(c(c(nc1[H])N([H])C([H])([H])[H])I)[H]
+[H]Oc1c([H])c([H])c([H])c(F)c1I
+[H]C1=C(N(C(=O)N(C1=O)[H])[H])I
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+[H]c1c(c(c(nc1[H])I)N([H])C([H])([H])[H])[H]
+[H]c1nc(C#N)c(I)c([H])c1[H]
+[H]C([H])([H])C1=C(C(=O)N(N1C([H])([H])[H])[H])I
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+[H]c1c(c(c(c(c1[H])C(=O)[H])I)[H])[H]
+[H]c1c(c(nc(c1[H])I)SC([H])([H])[H])[H]
+[H]C1=C(OC(=C1I)C(=O)[H])Br
+[H]C([H])([H])C1=C(C(=NN1C([H])([H])[H])N([H])[H])I
+[H]C1=NC(=C(N1[H])I)C([H])([H])C([H])([H])[N+]([H])([H])[H]
+[H]c1nc(C([H])([H])C([H])([H])N([H])[H])c(I)n1[H]
+[H]C1=NC(=C(N1[H])C([H])([H])C([H])([H])[N+]([H])([H])[H])I
+[H]c1nc(I)c(SC([H])([H])[H])c([H])c1[H]
+[H]Oc1c(I)c([H])c([H])c([H])c1N([H])[H]
+[H]c1c(c(c(c(c1[H])I)[H])N([H])C([H])([H])[H])[H]
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+[H]c1c(I)c(C(=O)N([H])[H])nn1[H]
+[H]C1=NN(C(=C1I)C(=O)N([H])[H])[H]
+[H]c1c(nc(c(c1I)[H])OC([H])([H])[H])[H]
+[H]c1nc(I)c(C#N)c([H])c1[H]
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+[H]c1c(c(c(c(c1[H])Cl)I)N([H])[H])[H]
+[H]c1c(c(c(c(c1[H])I)C([H])([H])[H])N([H])[H])[H]
+[H]C1=NC(=C(C(=O)N1[H])I)C([H])([H])[H]
+[H]c1c(c(nc(c1I)[H])[H])C#N
+[H]c1c(c(c(c(c1N([H])[H])I)[H])F)[H]
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+[H]c1nc([H])c(N([H])C([H])([H])[H])c(I)c1[H]
+[H]C1=NC(=O)N(C(=C1I)N([H])[H])[H]
+[H]C1=C(C(=NC(=O)N1[H])N([H])[H])I
+[H]c1c(nc(c(c1I)[H])C([H])([H])C([H])([H])[H])[H]
+[H]c1c(c(c(c(c1[H])OC([H])([H])[H])I)[H])[H]
+[H]c1nc([H])c(C([H])([H])N([H])[H])c(I)c1[H]
+[H]c1c(c(c(c(c1[H])C#N)I)[H])[H]
+[H]c1c(I)nc(C(=O)C([H])([H])[H])n1[H]
+[H]C1=C([N+](=C(N1[H])C(=O)C([H])([H])[H])[H])I
+[H]c1c(nc(c(c1SC([H])([H])[H])I)[H])[H]
+[H]C1=NN(C(=O)C(=C1[O-])I)[H]
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+[H]c1c(N([H])[H])nc(C([H])([H])[H])c(I)c1[H]
+[H]C1=C(C(=O)N(C(=C1I)[H])[H])C([H])([H])[H]
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+[H]c1c(c(c2c(c1[H])N=C(N2[H])I)[H])[H]
+[H]C(=O)C1=C(C(=NN1C([H])([H])[H])N([H])[H])I
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+[H]c1c([H])c([H])c(C([H])([H])N([H])C([H])([H])[H])c(I)c1[H]
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+[H]n1c(I)c(Cl)c(=O)n([H])c1=O
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\ No newline at end of file
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/full-environment.yaml b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/full-environment.yaml
new file mode 100644
index 00000000..752e70ac
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/full-environment.yaml
@@ -0,0 +1,383 @@
+name: i-frag-submit
+channels:
+  - openeye
+  - conda-forge
+  - defaults
+dependencies:
+  - ambertools=23.6=cuda_None_nompi_py312hc98840c_105
+  - annotated-types=0.7.0=pyhd8ed1ab_0
+  - anyio=4.4.0=pyhd8ed1ab_0
+  - appnope=0.1.4=pyhd8ed1ab_0
+  - apsw=3.46.0.1=py312h77d72a6_0
+  - archspec=0.2.3=pyhd8ed1ab_0
+  - argcomplete=3.5.0=pyhd8ed1ab_0
+  - argon2-cffi=23.1.0=pyhd8ed1ab_0
+  - argon2-cffi-bindings=21.2.0=py312hb553811_5
+  - arpack=3.9.1=nompi_hf81eadf_101
+  - arrow=1.3.0=pyhd8ed1ab_0
+  - asttokens=2.4.1=pyhd8ed1ab_0
+  - astunparse=1.6.3=pyhd8ed1ab_0
+  - async-lru=2.0.4=pyhd8ed1ab_0
+  - attrs=24.2.0=pyh71513ae_0
+  - babel=2.14.0=pyhd8ed1ab_0
+  - basis_set_exchange=0.10=pyhd8ed1ab_1
+  - beautifulsoup4=4.12.3=pyha770c72_0
+  - black=24.8.0=py312hb401068_0
+  - bleach=6.1.0=pyhd8ed1ab_0
+  - blosc=1.21.6=h7d75f6d_0
+  - boltons=24.0.0=pyhd8ed1ab_0
+  - brotli=1.1.0=h00291cd_2
+  - brotli-bin=1.1.0=h00291cd_2
+  - brotli-python=1.1.0=py312h5861a67_2
+  - bson=0.5.9=py_0
+  - bzip2=1.0.8=hfdf4475_7
+  - c-ares=1.33.1=h44e7173_0
+  - c-blosc2=2.15.1=hb9356d3_0
+  - ca-certificates=2024.8.30=h8857fd0_0
+  - cached-property=1.5.2=hd8ed1ab_1
+  - cached_property=1.5.2=pyha770c72_1
+  - cachetools=5.5.0=pyhd8ed1ab_0
+  - cairo=1.18.0=h37bd5c4_3
+  - certifi=2024.8.30=pyhd8ed1ab_0
+  - cffi=1.17.1=py312hf857d28_0
+  - cfgv=3.3.1=pyhd8ed1ab_0
+  - chardet=5.2.0=py312hb401068_2
+  - charset-normalizer=3.3.2=pyhd8ed1ab_0
+  - click=8.1.7=unix_pyh707e725_0
+  - cloudpickle=3.0.0=pyhd8ed1ab_0
+  - cmiles=0.1.6=ha770c72_2
+  - cmiles-base=0.1.6=pyhd8ed1ab_2
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - comm=0.2.2=pyhd8ed1ab_0
+  - conda=24.7.1=py312hb401068_0
+  - conda-libmamba-solver=24.7.0=pyhd8ed1ab_0
+  - conda-pack=0.8.0=pyhd8ed1ab_0
+  - conda-package-handling=2.3.0=pyh7900ff3_0
+  - conda-package-streaming=0.10.0=pyhd8ed1ab_0
+  - contourpy=1.3.0=py312hc5c4d5f_1
+  - coverage=7.6.1=py312hb553811_1
+  - cycler=0.12.1=pyhd8ed1ab_0
+  - debugpy=1.8.5=py312h5861a67_1
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - defusedxml=0.7.1=pyhd8ed1ab_0
+  - distlib=0.3.8=pyhd8ed1ab_0
+  - distro=1.9.0=pyhd8ed1ab_0
+  - entrypoints=0.4=pyhd8ed1ab_0
+  - exceptiongroup=1.2.2=pyhd8ed1ab_0
+  - execnet=2.1.1=pyhd8ed1ab_0
+  - executing=2.1.0=pyhd8ed1ab_0
+  - expat=2.6.3=hac325c4_0
+  - fftw=3.3.10=nompi_h292e606_110
+  - filelock=3.16.0=pyhd8ed1ab_0
+  - flake8=7.1.1=pyhd8ed1ab_0
+  - fmt=10.2.1=h7728843_0
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=h77eed37_2
+  - fontconfig=2.14.2=h5bb23bf_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - fonttools=4.53.1=py312hb553811_1
+  - forcebalance=1.9.6=py312hafa5229_2
+  - fqdn=1.5.1=pyhd8ed1ab_0
+  - freetype=2.12.1=h60636b9_2
+  - freetype-py=2.3.0=pyhd8ed1ab_0
+  - frozendict=2.4.4=py312hbd25219_0
+  - future=1.0.0=pyhd8ed1ab_0
+  - geometric=1.0.2=pyhd8ed1ab_0
+  - greenlet=3.0.3=py312hede676d_0
+  - h11=0.14.0=pyhd8ed1ab_0
+  - h2=4.1.0=pyhd8ed1ab_0
+  - hdf4=4.2.15=h8138101_7
+  - hdf5=1.14.3=nompi_h687a608_105
+  - hpack=4.0.0=pyh9f0ad1d_0
+  - httpcore=1.0.5=pyhd8ed1ab_0
+  - httpx=0.27.2=pyhd8ed1ab_0
+  - hyperframe=6.0.1=pyhd8ed1ab_0
+  - icu=75.1=h120a0e1_0
+  - identify=2.6.0=pyhd8ed1ab_0
+  - idna=3.8=pyhd8ed1ab_0
+  - importlib-metadata=8.4.0=pyha770c72_0
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+  - pillow=10.4.0=py312hbd70edc_0
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+  - pixman=0.43.4=h73e2aa4_0
+  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
+  - platformdirs=4.3.2=pyhd8ed1ab_0
+  - pluggy=1.5.0=pyhd8ed1ab_0
+  - pre-commit=3.8.0=pyha770c72_1
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+  - psutil=6.0.0=py312hb553811_1
+  - pthread-stubs=0.4=hc929b4f_1001
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.3=pyhd8ed1ab_0
+  - py-cpuinfo=9.0.0=pyhd8ed1ab_0
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+  - pyobjc-core=10.3.1=py312hab44e94_1
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+  - pysocks=1.7.1=pyha2e5f31_6
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+  - pytest-timeout=2.3.1=pyhd8ed1ab_1
+  - pytest-xdist=3.6.1=pyhd8ed1ab_0
+  - python=3.12.5=h37a9e06_0_cpython
+  - python-constraint=1.4.0=py_0
+  - python-dateutil=2.9.0=pyhd8ed1ab_0
+  - python-fastjsonschema=2.20.0=pyhd8ed1ab_0
+  - python-json-logger=2.0.7=pyhd8ed1ab_0
+  - python-tzdata=2024.1=pyhd8ed1ab_0
+  - python_abi=3.12=5_cp312
+  - pytz=2024.1=pyhd8ed1ab_0
+  - pyyaml=6.0.2=py312hb553811_1
+  - pyzmq=26.2.0=py312h54d5c6a_2
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+  - qcengine=0.30.0=pyhd8ed1ab_0
+  - qcportal=0.56=pyhd8ed1ab_1
+  - qhull=2020.2=h3c5361c_5
+  - qubekit=2.1.0=pyhd8ed1ab_0
+  - rdkit=2024.03.5=py312hcfd6466_3
+  - readline=8.2=h9e318b2_1
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+  - reproc-cpp=14.2.4.post0=h93d8f39_1
+  - requests=2.32.3=pyhd8ed1ab_0
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+  - rich=13.7.1=pyhd8ed1ab_0
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+  - scipy=1.14.1=py312he82a568_0
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+  - smirnoff-plugins=2024.07.0=pyhd8ed1ab_0
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+  - tk=8.6.13=h1abcd95_1
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+  - traitlets=5.14.3=pyhd8ed1ab_0
+  - truststore=0.9.2=pyhd8ed1ab_0
+  - types-python-dateutil=2.9.0.20240906=pyhd8ed1ab_0
+  - typing-extensions=4.12.2=hd8ed1ab_0
+  - typing_extensions=4.12.2=pyha770c72_0
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+  - tzdata=2024a=h8827d51_1
+  - ukkonen=1.0.1=py312hc5c4d5f_5
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+  - uri-template=1.3.0=pyhd8ed1ab_0
+  - urllib3=2.2.2=pyhd8ed1ab_1
+  - validators=0.34.0=pyhd8ed1ab_0
+  - virtualenv=20.26.4=pyhd8ed1ab_0
+  - wcwidth=0.2.13=pyhd8ed1ab_0
+  - webcolors=24.8.0=pyhd8ed1ab_0
+  - webencodings=0.5.1=pyhd8ed1ab_2
+  - websocket-client=1.8.0=pyhd8ed1ab_0
+  - wheel=0.44.0=pyhd8ed1ab_0
+  - widgetsnbextension=4.0.13=pyhd8ed1ab_0
+  - xmltodict=0.13.0=pyhd8ed1ab_0
+  - xorg-kbproto=1.0.7=h35c211d_1002
+  - xorg-libice=1.1.1=h0dc2134_0
+  - xorg-libsm=1.2.4=h0dc2134_0
+  - xorg-libx11=1.8.9=h7022169_1
+  - xorg-libxau=1.0.11=h0dc2134_0
+  - xorg-libxdmcp=1.1.3=h35c211d_0
+  - xorg-libxext=1.3.4=hb7f2c08_2
+  - xorg-libxt=1.3.0=h0dc2134_1
+  - xorg-xextproto=7.3.0=hb7f2c08_1003
+  - xorg-xproto=7.0.31=h35c211d_1007
+  - xz=5.2.6=h775f41a_0
+  - yaml=0.2.5=h0d85af4_2
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+  - zeromq=4.3.5=hb33e954_5
+  - zipp=3.20.1=pyhd8ed1ab_0
+  - zlib=1.3.1=h87427d6_1
+  - zlib-ng=2.2.1=hf036a51_0
+  - zstandard=0.23.0=py312h7122b0e_1
+  - zstd=1.5.6=h915ae27_0
+  - pip:
+      - amberutils==21.0
+      - edgembar==0.2
+      - mmpbsa-py==16.0
+      - packmol-memgen==2024.2.9
+      - pdb4amber==22.0
+      - pymsmt==22.0
+      - pytraj==2.0.6
+      - sander==22.0
+prefix: /Users/lexiemcisaac/miniconda3/envs/i-frag-submit
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/generate-dataset.ipynb b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/generate-dataset.ipynb
new file mode 100644
index 00000000..835a69dc
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/generate-dataset.ipynb
@@ -0,0 +1,670 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1fd519b4-ac84-4546-8413-0fccbc86540d",
+   "metadata": {},
+   "source": [
+    "# Dataset generation\n",
+    "\n",
+    "This notebook documents the creation of a dataset of small I-containing molecules to be optimized and later used for ESP generation. \n",
+    "\n",
+    "All I-containing molecules were selected from the ZINC, enamine, and CHEMBL datasets. These molecules were then fragmented, and small fragments containing I were kept. Protomers were then enumerated. The results of this process are stored in `enumerated.smi`. \n",
+    "\n",
+    "In this document we generate ELF conformers for each molecule and create an OptimizationDataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aa26fc7-5d75-4e95-af1e-20780a5cfee0",
+   "metadata": {},
+   "source": [
+    "# Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6160c2ad-5bdd-46aa-b66b-27a65e4a689d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:04.436619Z",
+     "iopub.status.busy": "2024-09-10T23:25:04.436327Z",
+     "iopub.status.idle": "2024-09-10T23:25:05.419283Z",
+     "shell.execute_reply": "2024-09-10T23:25:05.418783Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:04.436602Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenFF QCSubmit: 0.53.0\n",
+      "OpenFF Toolkit: 0.16.4\n",
+      "OpenEye: 2024.1.0\n",
+      "QCElemental: 0.28.0\n",
+      "QCPortal: 0.56\n"
+     ]
+    }
+   ],
+   "source": [
+    "import openff.qcsubmit\n",
+    "import openff.toolkit\n",
+    "import openeye\n",
+    "import qcelemental\n",
+    "import qcportal\n",
+    "\n",
+    "print(\"OpenFF QCSubmit:\", openff.qcsubmit.__version__)\n",
+    "print(\"OpenFF Toolkit:\", openff.toolkit.__version__)\n",
+    "print(\"OpenEye:\", openeye.__version__)\n",
+    "print(\"QCElemental:\", qcelemental.__version__)\n",
+    "print(\"QCPortal:\", qcportal.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a86eab00-2df2-4169-bbaf-44fea8879a22",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:05.420389Z",
+     "iopub.status.busy": "2024-09-10T23:25:05.420077Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.254055Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.253637Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:05.420364Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import tqdm\n",
+    "\n",
+    "from openff.units import unit\n",
+    "\n",
+    "from openff.toolkit import Molecule\n",
+    "from openff.toolkit.utils import OpenEyeToolkitWrapper, ToolkitRegistry\n",
+    "\n",
+    "from openff.qcsubmit.datasets import OptimizationDataset\n",
+    "from openff.qcsubmit.factories import OptimizationDatasetFactory\n",
+    "\n",
+    "from qcelemental.models.results import WavefunctionProtocolEnum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8228662-e606-4a07-9a16-9594ecc55c3c",
+   "metadata": {},
+   "source": [
+    "# Setting up dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "afaa6f02-ef9b-45d7-967a-fb0204e04c33",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.255069Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.254817Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.257627Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.257225Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.255056Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset_factory = OptimizationDatasetFactory()\n",
+    "provenance = dataset_factory.provenance(ToolkitRegistry([OpenEyeToolkitWrapper]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "38e4fa20-357a-44f2-a177-abda9ceafe29",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.258644Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.258463Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.263729Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.263273Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.258631Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset = OptimizationDataset(\n",
+    "    dataset_name=\"OpenFF Iodine Fragment Opt v1.0\",\n",
+    "    dataset_tagline=\"B3LYP-D3BJ/DZVP optimized conformers for a variety of I-containing fragment molecules\", # add method\n",
+    "    description=(\n",
+    "        \"A dataset containing fragments of molecules from the \"\n",
+    "        \"Zinc, Enamine 10240, Enamine 50240, and CHEMBL datasets optimized at the B3LYP-D3BJ/DZVP level of theory. \"\n",
+    "        \"Molecules containing I were fragmented into small fragments. \"\n",
+    "        \"Fragments containing I then had protomers enumerated.\\n\\n\"\n",
+    "        \"For each resulting molecule, a set of up to 5 conformers were generated by:\\n\"\n",
+    "        \"  * generating a set of up to 1000 conformers with a RMS cutoff of 0.5 Å \"\n",
+    "        \"using the OpenEye backend of the OpenFF toolkit\\n\"\n",
+    "        \"  * applying ELF conformer selection (max 5 conformers) using OpenEye\\n\\n\"\n",
+    "    ),\n",
+    "    provenance=provenance\n",
+    ")\n",
+    "dataset.metadata.submitter = \"amcisaac\"\n",
+    "dataset.metadata.long_description_url = (\n",
+    "        \"https://github.com/openforcefield/qca-dataset-submission/tree/master/\"\n",
+    "        \"submissions/\"\n",
+    "        \"2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0\"\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a8757cdb-0c3e-4560-b4fa-59c03366a2c0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.264527Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.264388Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.267468Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.267015Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.264514Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'default': QCSpec(method='B3LYP-D3BJ', basis='DZVP', program='psi4', spec_name='default', spec_description='Standard OpenFF optimization quantum chemistry specification.', store_wavefunction=<WavefunctionProtocolEnum.none: 'none'>, implicit_solvent=None, maxiter=200, scf_properties=[<SCFProperties.Dipole: 'dipole'>, <SCFProperties.Quadrupole: 'quadrupole'>, <SCFProperties.WibergLowdinIndices: 'wiberg_lowdin_indices'>, <SCFProperties.MayerIndices: 'mayer_indices'>], keywords={})}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset.qc_specifications)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f95c526d-634e-46ed-8f7f-13cdc70844d5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.270235Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.270031Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.272542Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.272199Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.270220Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A dataset containing fragments of molecules from the Zinc, Enamine 10240, Enamine 50240, and CHEMBL datasets optimized at the B3LYP-D3BJ/DZVP level of theory. Molecules containing I were fragmented into small fragments. Fragments containing I then had protomers enumerated.\n",
+      "\n",
+      "For each resulting molecule, a set of up to 5 conformers were generated by:\n",
+      "  * generating a set of up to 1000 conformers with a RMS cutoff of 0.5 Å using the OpenEye backend of the OpenFF toolkit\n",
+      "  * applying ELF conformer selection (max 5 conformers) using OpenEye\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset.description)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8139334-9894-4e0b-b9b2-40d039ddf9ae",
+   "metadata": {},
+   "source": [
+    "# Generate conformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a398badb-2831-4652-85ee-c41addf81619",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.273432Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.273206Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.276889Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.276388Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.273419Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"enumerated.smi\", \"r\") as f:\n",
+    "    smiles = [x.strip() for x in f.readlines()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b85c5e9f-4051-4151-bf0f-e8618f568c06",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.277653Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.277508Z",
+     "iopub.status.idle": "2024-09-10T23:25:09.281154Z",
+     "shell.execute_reply": "2024-09-10T23:25:09.280678Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.277641Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Taken from the multi-Br optimization submission\n",
+    "def generate_conformers(smiles: str) -> Molecule:\n",
+    "    wrapper = OpenEyeToolkitWrapper()\n",
+    "    \n",
+    "    mol = Molecule.from_smiles(\n",
+    "        smiles,\n",
+    "        allow_undefined_stereo=True,\n",
+    "        toolkit_registry=wrapper,\n",
+    "    )\n",
+    "    # generate max 1000 conformers with OpenEye\n",
+    "    mol.generate_conformers(\n",
+    "        n_conformers=1000,\n",
+    "        rms_cutoff=0.5 * unit.angstrom,\n",
+    "        toolkit_registry=wrapper,\n",
+    "    )\n",
+    "    \n",
+    "    # prune based on ELF method, max 5 conformers output\n",
+    "    mol.apply_elf_conformer_selection(\n",
+    "        percentage=2.0,\n",
+    "        limit=5,\n",
+    "        toolkit_registry=wrapper\n",
+    "    )\n",
+    "    \n",
+    "    assert mol.n_conformers > 0\n",
+    "    \n",
+    "    return mol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "92555fd2-a935-43b5-ac92-26dbdbbf6cdc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:25:09.282048Z",
+     "iopub.status.busy": "2024-09-10T23:25:09.281867Z",
+     "iopub.status.idle": "2024-09-10T23:26:23.736668Z",
+     "shell.execute_reply": "2024-09-10T23:26:23.736135Z",
+     "shell.execute_reply.started": "2024-09-10T23:25:09.282034Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "generating conformers:   0%|                                                        | 1/526 [00:01<11:40,  1.33s/it]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:   4%|██▍                                                    | 23/526 [00:04<01:03,  7.87it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:   5%|██▌                                                    | 24/526 [00:04<01:08,  7.37it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:   6%|███▎                                                   | 32/526 [00:05<01:04,  7.66it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  11%|██████                                                 | 58/526 [00:08<01:01,  7.63it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  17%|█████████▌                                             | 92/526 [00:13<00:59,  7.32it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  23%|████████████▋                                         | 123/526 [00:17<00:51,  7.78it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  27%|██████████████▎                                       | 140/526 [00:19<00:51,  7.50it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  32%|█████████████████▏                                    | 168/526 [00:23<00:48,  7.45it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  37%|███████████████████▋                                  | 192/526 [00:27<00:50,  6.63it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  61%|█████████████████████████████████                     | 322/526 [00:45<00:29,  6.92it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  72%|██████████████████████████████████████▉               | 379/526 [00:53<00:19,  7.44it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  72%|███████████████████████████████████████               | 380/526 [00:53<00:19,  7.41it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  73%|███████████████████████████████████████▏              | 382/526 [00:53<00:19,  7.38it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  75%|████████████████████████████████████████▌             | 395/526 [00:55<00:18,  7.14it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  75%|████████████████████████████████████████▋             | 396/526 [00:55<00:18,  7.11it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  77%|█████████████████████████████████████████▌            | 405/526 [00:56<00:17,  7.01it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  79%|██████████████████████████████████████████▌           | 414/526 [00:58<00:15,  7.06it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  87%|██████████████████████████████████████████████▋       | 455/526 [01:03<00:10,  7.06it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  88%|███████████████████████████████████████████████▎      | 461/526 [01:04<00:09,  7.09it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  89%|███████████████████████████████████████████████▊      | 466/526 [01:05<00:08,  7.23it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  89%|███████████████████████████████████████████████▉      | 467/526 [01:05<00:08,  6.82it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  89%|████████████████████████████████████████████████▏     | 469/526 [01:05<00:08,  6.71it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  92%|█████████████████████████████████████████████████▊    | 485/526 [01:08<00:06,  6.78it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  92%|█████████████████████████████████████████████████▉    | 486/526 [01:08<00:06,  6.56it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  94%|██████████████████████████████████████████████████▌   | 492/526 [01:09<00:04,  7.07it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  94%|██████████████████████████████████████████████████▊   | 495/526 [01:09<00:04,  6.80it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers:  94%|██████████████████████████████████████████████████▉   | 496/526 [01:10<00:05,  5.19it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers: 100%|█████████████████████████████████████████████████████▉| 525/526 [01:14<00:00,  7.11it/s]Warning: : Failed due to unspecified stereochemistry\n",
+      "generating conformers: 100%|██████████████████████████████████████████████████████| 526/526 [01:14<00:00,  7.07it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for smi in tqdm.tqdm(smiles, desc=\"generating conformers\"):\n",
+    "    mol = generate_conformers(smi)\n",
+    "    dataset.add_molecule(\n",
+    "        dataset_factory.create_index(molecule=mol),\n",
+    "        mol\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d231cc1-7244-4a6a-bf66-e10cb0599b11",
+   "metadata": {},
+   "source": [
+    "# Exporting dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "845d69f3-b940-4a73-9c54-43996ec6770f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:23.737667Z",
+     "iopub.status.busy": "2024-09-10T23:26:23.737476Z",
+     "iopub.status.idle": "2024-09-10T23:26:25.308064Z",
+     "shell.execute_reply": "2024-09-10T23:26:25.307655Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:23.737650Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset.export_dataset(\"dataset.json.bz2\")\n",
+    "dataset.molecules_to_file('dataset.smi', 'smi')\n",
+    "dataset.visualize(\"dataset.pdf\", columns=8)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a657203-5a63-4b9b-a29b-20293026aa2e",
+   "metadata": {},
+   "source": [
+    "# Dataset information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "38e6f7ba-3f64-4ef4-aa97-fa5c405e180e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:25.308871Z",
+     "iopub.status.busy": "2024-09-10T23:26:25.308738Z",
+     "iopub.status.idle": "2024-09-10T23:26:25.660037Z",
+     "shell.execute_reply": "2024-09-10T23:26:25.659632Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:25.308856Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from collections import Counter\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d1014446-cfeb-43b0-b30b-0b7147ac1ef1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:25.661039Z",
+     "iopub.status.busy": "2024-09-10T23:26:25.660692Z",
+     "iopub.status.idle": "2024-09-10T23:26:27.723170Z",
+     "shell.execute_reply": "2024-09-10T23:26:27.722787Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:25.661024Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Number of unique molecules: 526\n",
+      "* Number of conformers: 531\n",
+      "* Number of conformers per molecule (min, mean, max): 1, 1.01, 5\n",
+      "* Molecular weight (min, mean, max): (155.97, 242.52, 316.94)\n",
+      "* Charges: [-1.0, 0.0, 1.0, 2.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "confs = np.array([len(mol.conformers) for mol in dataset.molecules])\n",
+    "\n",
+    "print(\"* Number of unique molecules:\", dataset.n_molecules)\n",
+    "print(\"* Number of conformers:\", sum(confs))\n",
+    "print(\"* Number of conformers per molecule (min, mean, max): \"\n",
+    "      f\"{confs.min()}, {confs.mean():.2f}, {confs.max()}\")\n",
+    "\n",
+    "masses = [\n",
+    "    [\n",
+    "        sum([atom.mass.m for atom in molecule.atoms])\n",
+    "        for molecule in dataset.molecules\n",
+    "    ]\n",
+    "]\n",
+    "print(f'* Molecular weight (min, mean, max): ({np.min(np.array(masses)):.2f}, {np.mean(np.array(masses)):.2f}, {np.max(np.array(masses)):.2f})')\n",
+    "print(\"* Charges:\", sorted(set(m.total_charge.m for m in dataset.molecules)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4d1e2b6e-8b55-4dfe-802a-04ba522f3a56",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:27.725239Z",
+     "iopub.status.busy": "2024-09-10T23:26:27.725115Z",
+     "iopub.status.idle": "2024-09-10T23:26:27.728651Z",
+     "shell.execute_reply": "2024-09-10T23:26:27.728242Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:27.725226Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Metadata\n",
+      "* Elements: {S, C, N, F, I, Cl, H, O, Br}\n",
+      "* Spec: default\n",
+      "\t* basis: DZVP\n",
+      "\t* implicit_solvent: None\n",
+      "\t* keywords: {}\n",
+      "\t* maxiter: 200\n",
+      "\t* method: B3LYP-D3BJ\n",
+      "\t* program: psi4\n",
+      "\t* SCF properties:\n",
+      "\t\t* dipole\n",
+      "\t\t* quadrupole\n",
+      "\t\t* wiberg_lowdin_indices\n",
+      "\t\t* mayer_indices\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"## Metadata\")\n",
+    "print(f\"* Elements: {{{', '.join(dataset.metadata.dict()['elements'])}}}\")\n",
+    "\n",
+    "def print_field(od, field): print(f\"\\t* {field}: {od[field]}\")\n",
+    "\n",
+    "fields = [\"basis\", \"implicit_solvent\", \"keywords\", \"maxiter\", \"method\", \"program\"]\n",
+    "for spec, obj in dataset.qc_specifications.items():\n",
+    "    od = obj.dict()\n",
+    "    print(\"* Spec:\", spec)\n",
+    "    for field in fields:\n",
+    "        print_field(od, field)\n",
+    "    print(\"\\t* SCF properties:\")\n",
+    "    for field in od[\"scf_properties\"]:\n",
+    "        print(f\"\\t\\t* {field}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5f6afdb8-888e-43a4-ad91-7dde944f29d6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:27.729352Z",
+     "iopub.status.busy": "2024-09-10T23:26:27.729230Z",
+     "iopub.status.idle": "2024-09-10T23:26:27.818848Z",
+     "shell.execute_reply": "2024-09-10T23:26:27.818451Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:27.729339Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q2bOn83QWAAC4uIU07Gzfvl2pqanO6ylTpkiSxo0bp6VLl+rxxx9XeXm5JkyYoOLiYvXv31/r169XTEyMc8z8+fMVFRWl0aNHq7y8XIMHD9bSpUvVqFGjC94PAAAIPy5jjAl1EaFWUlIij8cjv9/P/TvAGSROXxvqEi4Kh+YOD3UJQNg725/fYXvPDgAAQH0g7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsFqdwk7Hjh317bffVhv//vvv1bFjx/MuCgAAoL7UKewcOnRIJ0+erDYeCAR05MiR8y4KAACgvpzTb1Bes2aN8+d169bJ4/E4r0+ePKkPPvhAiYmJ9VYcAADA+TqnsHPLLbdIklwul8aNGxe0Lzo6WomJiXr++efrrTgAAIDzdU5hp6qqStKPn0i+bds2tW7dukGKAgAAqC91+iDQgoKC+q4DAACgQdT5U88/+OADffDBByoqKnJWfE7585//fN6FAQAA1Ic6hZ1nnnlGs2fPVt++fXXZZZfJ5XLVd10AAAD1ok5hZ9GiRVq6dKnGjh1b3/UAAADUqzr9np2KigoNGDCgvmsBAACod3UKOw888ICWL19e37UAAADUuzq9jXXixAktXrxYGzZs0JVXXqno6Oig/ZmZmfVSHAAAwPmqU9jZvXu3evfuLUnKz88P2sfNygAAIJzUKezk5OTUdx0AAAANok737AAAAESKOq3spKamnvbtqg8//LDOBQEAANSnOoWdU/frnFJZWaldu3YpPz+/2geEAgAAhFKdws78+fNrHJ81a5bKysrOqyAAAID6VK/37Nx99918LhYAAAgr9Rp2Pv74YzVp0qQ+TwkAAHBe6vQ21qhRo4JeG2N09OhRbd++XU8//XS9FAYAAFAf6hR2PB5P0OtLLrlEXbp00ezZs5WWllYvhQEAANSHOoWdJUuW1HcdAAAADaJOYeeUvLw87du3Ty6XS926ddNVV11VX3UBAADUizqFnaKiIt1xxx3auHGjWrRoIWOM/H6/UlNTtWLFCl166aX1XScAAECd1OlprEmTJqmkpER79+7Vd999p+LiYuXn56ukpESPPPJIfdcIAABQZ3UKO++//74WLlyorl27OmPdunXTyy+/rPfee6/eivvhhx/01FNPKSkpSU2bNlXHjh01e/ZsVVVVOXOMMZo1a5Z8Pp+aNm2qQYMGae/evfVWAwAAiGx1CjtVVVWKjo6uNh4dHR0URM7Xc889p0WLFmnBggXat2+f5s2bp3//93/XSy+95MyZN2+eMjMztWDBAm3btk1er1dDhw5VaWlpvdUBAAAiV53Czg033KDf/va3+uabb5yxI0eO6NFHH9XgwYPrrbiPP/5YN998s4YPH67ExETddtttSktL0/bt2yX9uKqTlZWlmTNnatSoUerRo4eWLVum48ePa/ny5fVWBwAAiFx1CjsLFixQaWmpEhMTdfnll6tTp05KSkpSaWlp0KrL+Ro4cKA++OADHThwQJL02WefaevWrfrVr34lSSooKFBhYWHQ7/Zxu91KSUlRbm5urecNBAIqKSkJ2gAAgJ3q9DRWQkKCduzYoezsbH3++ecyxqhbt24aMmRIvRb3xBNPyO/364orrlCjRo108uRJPfvss7rzzjslSYWFhZKk+Pj4oOPi4+P15Zdf1nrejIwMPfPMM/VaKwAACE/ntLLz4Ycfqlu3bs5KyNChQzVp0iQ98sgj6tevn7p3764tW7bUW3FvvfWW3njjDS1fvlw7duzQsmXL9Ic//EHLli0LmudyuYJeG2Oqjf3UjBkz5Pf7ne3w4cP1VjMAAAgv57Syk5WVpX/7t39TbGxstX0ej0fjx49XZmamfvnLX9ZLcdOmTdP06dN1xx13SJJ69uypL7/8UhkZGRo3bpy8Xq+kH1d4LrvsMue4oqKiaqs9P+V2u+V2u+ulRgAAEN7OaWXns88+04033ljr/rS0NOXl5Z13UaccP35cl1wSXGKjRo2cJ76SkpLk9XqVnZ3t7K+oqNCmTZs0YMCAeqsDAABErnNa2fnnP/9Z4yPnzsmiovS///u/513UKSNGjNCzzz6r9u3bq3v37tq5c6cyMzN13333Sfrx7avJkydrzpw5Sk5OVnJysubMmaNmzZppzJgx9VYHAACIXOcUdtq2bas9e/aoU6dONe7fvXt30NtJ5+ull17S008/rQkTJqioqEg+n0/jx4/X7373O2fO448/rvLyck2YMEHFxcXq37+/1q9fr5iYmHqrAwAARC6XMcac7eRJkyZp48aN2rZtm5o0aRK0r7y8XNdcc41SU1P14osv1nuhDamkpEQej0d+v7/G+5EA/J/E6WtDXcJF4dDc4aEuAQh7Z/vz+5xWdp566imtXLlSnTt31sSJE9WlSxe5XC7t27dPL7/8sk6ePKmZM2eed/EAAAD15ZzCTnx8vHJzc/XQQw9pxowZOrUo5HK5lJ6erldeeeW0T0EBAABcaOf8SwU7dOigd999V8XFxTp48KCMMUpOTlbLli0boj4AAIDzUqffoCxJLVu2VL9+/eqzFgAAgHpXp8/GAgAAiBSEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLWoUBcAALBD4vS1oS7hnB2aOzzUJeACYGUHAABYjbADAACsFvZh58iRI7r77rvVqlUrNWvWTL1791ZeXp6z3xijWbNmyefzqWnTpho0aJD27t0bwooBAEA4CeuwU1xcrOuuu07R0dF677339D//8z96/vnn1aJFC2fOvHnzlJmZqQULFmjbtm3yer0aOnSoSktLQ1c4AAAIG2F9g/Jzzz2nhIQELVmyxBlLTEx0/myMUVZWlmbOnKlRo0ZJkpYtW6b4+HgtX75c48ePr/G8gUBAgUDAeV1SUtIwDQAAgJAL65WdNWvWqG/fvvrXf/1XtWnTRldddZX+9Kc/OfsLCgpUWFiotLQ0Z8ztdislJUW5ubm1njcjI0Mej8fZEhISGrQPAAAQOmEddv7xj39o4cKFSk5O1rp16/Tggw/qkUce0WuvvSZJKiwslCTFx8cHHRcfH+/sq8mMGTPk9/ud7fDhww3XBAAACKmwfhurqqpKffv21Zw5cyRJV111lfbu3auFCxfqnnvucea5XK6g44wx1cZ+yu12y+12N0zRAFAPIvF31gDhKqxXdi677DJ169YtaKxr16766quvJEler1eSqq3iFBUVVVvtAQAAF6ewDjvXXXed9u/fHzR24MABdejQQZKUlJQkr9er7OxsZ39FRYU2bdqkAQMGXNBaAQBAeArrt7EeffRRDRgwQHPmzNHo0aP16aefavHixVq8eLGkH9++mjx5subMmaPk5GQlJydrzpw5atasmcaMGRPi6gEAQDgI67DTr18/rVq1SjNmzNDs2bOVlJSkrKws3XXXXc6cxx9/XOXl5ZowYYKKi4vVv39/rV+/XjExMSGsHAAAhAuXMcaEuohQKykpkcfjkd/vV2xsbKjLAcIaN87CJnwQaGQ725/fYX3PDgAAwPki7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLWwfvQcsB1PNgFAw2NlBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArBZRYScjI0Mul0uTJ092xowxmjVrlnw+n5o2bapBgwZp7969oSsSAACElYgJO9u2bdPixYt15ZVXBo3PmzdPmZmZWrBggbZt2yav16uhQ4eqtLQ0RJUCAIBwEhFhp6ysTHfddZf+9Kc/qWXLls64MUZZWVmaOXOmRo0apR49emjZsmU6fvy4li9fXuv5AoGASkpKgjYAAGCniAg7Dz/8sIYPH64hQ4YEjRcUFKiwsFBpaWnOmNvtVkpKinJzc2s9X0ZGhjwej7MlJCQ0WO0AACC0wj7srFixQjt27FBGRka1fYWFhZKk+Pj4oPH4+HhnX01mzJghv9/vbIcPH67fogEAQNiICnUBp3P48GH99re/1fr169WkSZNa57lcrqDXxphqYz/ldrvldrvrrU4AABC+wnplJy8vT0VFRerTp4+ioqIUFRWlTZs26cUXX1RUVJSzovPzVZyioqJqqz0AAODiFNZhZ/DgwdqzZ4927drlbH379tVdd92lXbt2qWPHjvJ6vcrOznaOqaio0KZNmzRgwIAQVg4AAMJFWL+NFRMTox49egSNNW/eXK1atXLGJ0+erDlz5ig5OVnJycmaM2eOmjVrpjFjxoSiZAAAEGbCOuycjccff1zl5eWaMGGCiouL1b9/f61fv14xMTGhLg0AAIQBlzHGhLqIUCspKZHH45Hf71dsbGyoy8FFJHH62lCXAFzUDs0dHuoScB7O9ud3WN+zAwAAcL4IOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLWoUBcA1JfE6WtDXQIAIAyxsgMAAKxG2AEAAFYj7AAAAKsRdgAAgNXCOuxkZGSoX79+iomJUZs2bXTLLbdo//79QXOMMZo1a5Z8Pp+aNm2qQYMGae/evSGqGAAAhJuwDjubNm3Sww8/rE8++UTZ2dn64YcflJaWpmPHjjlz5s2bp8zMTC1YsEDbtm2T1+vV0KFDVVpaGsLKAQBAuAjrR8/ff//9oNdLlixRmzZtlJeXp+uvv17GGGVlZWnmzJkaNWqUJGnZsmWKj4/X8uXLNX78+FCUDQAAwkhYr+z8nN/vlyTFxcVJkgoKClRYWKi0tDRnjtvtVkpKinJzc2s9TyAQUElJSdAGAADsFDFhxxijKVOmaODAgerRo4ckqbCwUJIUHx8fNDc+Pt7ZV5OMjAx5PB5nS0hIaLjCAQBASEVM2Jk4caJ2796tN998s9o+l8sV9NoYU23sp2bMmCG/3+9shw8frvd6AQBAeAjre3ZOmTRpktasWaPNmzerXbt2zrjX65X04wrPZZdd5owXFRVVW+35KbfbLbfb3XAFAwCAsBHWKzvGGE2cOFErV67Uhx9+qKSkpKD9SUlJ8nq9ys7OdsYqKiq0adMmDRgw4EKXCwAAwlBYr+w8/PDDWr58uf76178qJibGuQ/H4/GoadOmcrlcmjx5subMmaPk5GQlJydrzpw5atasmcaMGRPi6gEAQDgI67CzcOFCSdKgQYOCxpcsWaJ7771XkvT444+rvLxcEyZMUHFxsfr376/169crJibmAlcLAADCUViHHWPMGee4XC7NmjVLs2bNaviCAABAxAnre3YAAADOV1iv7AAA0JASp68NdQnn7NDc4aEuIeKwsgMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALBaVKgLsF3i9LWhLqFODs0dHuoSAAA1iMSfK6H+mcLKDgAAsBphBwAAWI2wAwAArMY9O6hRJL4nDABATVjZAQAAViPsAAAAq1kTdl555RUlJSWpSZMm6tOnj7Zs2RLqkgAAQBiwIuy89dZbmjx5smbOnKmdO3fql7/8pYYNG6avvvoq1KUBAIAQsyLsZGZm6v7779cDDzygrl27KisrSwkJCVq4cGGoSwMAACEW8U9jVVRUKC8vT9OnTw8aT0tLU25ubo3HBAIBBQIB57Xf75cklZSU1Ht9VYHj9X5OAAAiSUP8fP3peY0xp50X8WHn//2//6eTJ08qPj4+aDw+Pl6FhYU1HpORkaFnnnmm2nhCQkKD1AgAwMXMk9Ww5y8tLZXH46l1f8SHnVNcLlfQa2NMtbFTZsyYoSlTpjivq6qq9N1336lVq1a1HnMhlJSUKCEhQYcPH1ZsbGzI6mgo9BfZ6C+y0V9ko7+aGWNUWloqn8932nkRH3Zat26tRo0aVVvFKSoqqrbac4rb7Zbb7Q4aa9GiRUOVeM5iY2Ot/Md8Cv1FNvqLbPQX2eivutOt6JwS8TcoN27cWH369FF2dnbQeHZ2tgYMGBCiqgAAQLiI+JUdSZoyZYrGjh2rvn376tprr9XixYv11Vdf6cEHHwx1aQAAIMSsCDu33367vv32W82ePVtHjx5Vjx499O6776pDhw6hLu2cuN1u/f73v6/2Fpst6C+y0V9ko7/IRn/nx2XO9LwWAABABIv4e3YAAABOh7ADAACsRtgBAABWI+wAAACrEXYa2ObNmzVixAj5fD65XC6tXr262px9+/Zp5MiR8ng8iomJ0S9+8YugT2wPBAKaNGmSWrdurebNm2vkyJH6+uuvL2AXtTtTf2VlZZo4caLatWunpk2bqmvXrtU+oDVc+8vIyFC/fv0UExOjNm3a6JZbbtH+/fuD5hhjNGvWLPl8PjVt2lSDBg3S3r17g+aEa3/SmXusrKzUE088oZ49e6p58+by+Xy655579M033wSdJ1x7PJvv4U+NHz9eLpdLWVlZQeOR3l+kXmPOpr9IvsYsXLhQV155pfOL9K699lq99957zv5Iv76crr8Lfm0xaFDvvvuumTlzpnn77beNJLNq1aqg/QcPHjRxcXFm2rRpZseOHebvf/+7eeedd8w///lPZ86DDz5o2rZta7Kzs82OHTtMamqq6dWrl/nhhx8ucDfVnam/Bx54wFx++eUmJyfHFBQUmD/+8Y+mUaNGZvXq1c6ccO0vPT3dLFmyxOTn55tdu3aZ4cOHm/bt25uysjJnzty5c01MTIx5++23zZ49e8ztt99uLrvsMlNSUuLMCdf+jDlzj99//70ZMmSIeeutt8znn39uPv74Y9O/f3/Tp0+foPOEa49n8z08ZdWqVaZXr17G5/OZ+fPnB+2L5P4i+RpzNv1F8jVmzZo1Zu3atWb//v1m//795sknnzTR0dEmPz/fGBP515fT9Xehry2EnQuopjBw++23m7vvvrvWY77//nsTHR1tVqxY4YwdOXLEXHLJJeb9999vqFLrpKb+unfvbmbPnh00dvXVV5unnnrKGBNZ/RUVFRlJZtOmTcYYY6qqqozX6zVz58515pw4ccJ4PB6zaNEiY0xk9WdM9R5r8umnnxpJ5ssvvzTGRFaPtfX39ddfm7Zt25r8/HzToUOHoLAT6f3ZdI2pqT+brjHGGNOyZUvzH//xH1ZeX4z5v/5q0pDXFt7GCqGqqiqtXbtWnTt3Vnp6utq0aaP+/fsHvRWUl5enyspKpaWlOWM+n089evRQbm5uCKo+NwMHDtSaNWt05MgRGWOUk5OjAwcOKD09XVJk9ef3+yVJcXFxkqSCggIVFhYG1e52u5WSkuLUHkn9SdV7rG2Oy+VyPk8uknqsqb+qqiqNHTtW06ZNU/fu3asdE8n92XaNqen7Z8s15uTJk1qxYoWOHTuma6+91rrry8/7q0lDXlsIOyFUVFSksrIyzZ07VzfeeKPWr1+vW2+9VaNGjdKmTZskSYWFhWrcuLFatmwZdGx8fHy1Dz8NRy+++KK6deumdu3aqXHjxrrxxhv1yiuvaODAgZIipz9jjKZMmaKBAweqR48ekuTU9/MPnP1p7ZHSn1Rzjz934sQJTZ8+XWPGjHE+rC9Seqytv+eee05RUVF65JFHajwukvuz6RpT2/cv0q8xe/bs0b/8y7/I7XbrwQcf1KpVq9StWzdrri+19fdzDX1tseLjIiJVVVWVJOnmm2/Wo48+Kknq3bu3cnNztWjRIqWkpNR6rDFGLpfrgtR5Pl588UV98sknWrNmjTp06KDNmzdrwoQJuuyyyzRkyJBajwu3/iZOnKjdu3dr69at1fb9vM6zqT3c+pNO36P04w2Fd9xxh6qqqvTKK6+c8Xzh1mNN/eXl5emFF17Qjh07zrnWSOjPpmtMbf8+I/0a06VLF+3atUvff/+93n77bY0bN84JolLkX19q6++ngedCXFtY2Qmh1q1bKyoqqlrK7dq1q/OkhNfrVUVFhYqLi4PmFBUVVUv84aa8vFxPPvmkMjMzNWLECF155ZWaOHGibr/9dv3hD3+QFBn9TZo0SWvWrFFOTo7atWvnjHu9Xkmq9j+Mn9YeCf1Jtfd4SmVlpUaPHq2CggJlZ2c7//OSIqPH2vrbsmWLioqK1L59e0VFRSkqKkpffvmlpk6dqsTEREmR3Z8t15ja+rPhGtO4cWN16tRJffv2VUZGhnr16qUXXnjBmutLbf2dcqGuLYSdEGrcuLH69etX7VHKAwcOOB9i2qdPH0VHRys7O9vZf/ToUeXn52vAgAEXtN5zVVlZqcrKSl1ySfA/s0aNGjn/4wzn/owxmjhxolauXKkPP/xQSUlJQfuTkpLk9XqDaq+oqNCmTZuc2sO5P+nMPUr/dzH64osvtGHDBrVq1Spofzj3eKb+xo4dq927d2vXrl3O5vP5NG3aNK1bt05SZPcX6deYM/UX6deYmhhjFAgErLi+1ORUf9IFvrac0+3MOGelpaVm586dZufOnUaSyczMNDt37nTuNl+5cqWJjo42ixcvNl988YV56aWXTKNGjcyWLVucczz44IOmXbt2ZsOGDWbHjh3mhhtuCJtHC8/UX0pKiunevbvJyckx//jHP8ySJUtMkyZNzCuvvOKcI1z7e+ihh4zH4zEbN240R48edbbjx487c+bOnWs8Ho9ZuXKl2bNnj7nzzjtrfDQ0HPsz5sw9VlZWmpEjR5p27dqZXbt2Bc0JBALOecK1x7P5Hv7cz5/GMiay+4vka8zZ9BfJ15gZM2aYzZs3m4KCArN7927z5JNPmksuucSsX7/eGBP515fT9Xehry2EnQaWk5NjJFXbxo0b58x59dVXTadOnUyTJk1Mr169gn4/hDHGlJeXm4kTJ5q4uDjTtGlTc9NNN5mvvvrqAndSszP1d/ToUXPvvfcan89nmjRpYrp06WKef/55U1VV5ZwjXPurqS9JZsmSJc6cqqoq8/vf/954vV7jdrvN9ddfb/bs2RN0nnDtz5gz91hQUFDrnJycHOc84drj2XwPf66msBPp/UXqNeZs+ovka8x9991nOnToYBo3bmwuvfRSM3jwYCfoGBP515fT9Xehry0uY4w5t7UgAACAyME9OwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AC4aFRUV6tSpkz766KMGOf9tt92mzMzMBjk3gLoj7AC4oO699165XC49+OCD1fZNmDBBLpdL9957ryRp0aJFiomJ0Q8//ODMKSsrU3R0tH75y18GHbtlyxa5XC4dOHCg1q+9ePFidejQQdddd50z5nK5nK158+ZKTk7Wvffeq7y8vHPu7Xe/+52effZZlZSUnPOxABoOYQfABZeQkKAVK1aovLzcGTtx4oTefPNNtW/f3hlLTU1VWVmZtm/f7oxt2bJFXq9X27Zt0/Hjx53xjRs3yufzqXPnzrV+3ZdeekkPPPBAtfElS5bo6NGj2rt3r15++WWVlZWpf//+eu21186pryuvvFKJiYn6y1/+ck7HAWhYhB0AF9zVV1+t9u3ba+XKlc7YypUrlZCQoKuuusoZ69Kli3w+nzZu3OiMbdy4UTfffLMuv/xy5ebmBo2npqbW+jV37NihgwcPavjw4dX2tWjRQl6vV4mJiUpLS9N//dd/6a677tLEiRNVXFwsSfr222915513ql27dmrWrJl69uypN998s9q5Ro4cWeM4gNAh7AAIiV//+tdasmSJ8/rPf/6z7rvvvmrzBg0apJycHOd1Tk6OBg0apJSUFGe8oqJCH3/88WnDzubNm9W5c2fFxsaeVX2PPvqoSktLlZ2dLenHlac+ffronXfeUX5+vn7zm99o7Nix+tvf/hZ03DXXXKNPP/1UgUDgrL4OgIZH2AEQEmPHjtXWrVt16NAhffnll/roo4909913V5s3aNAgffTRR/rhhx9UWlqqnTt36vrrr1dKSoqz4vPJJ5+ovLz8tGHn0KFD8vl8Z13fFVdc4RwnSW3bttVjjz2m3r17q2PHjpo0aZLS09P1n//5n0HHtW3bVoFAQIWFhWf9tQA0rKhQFwDg4tS6dWsNHz5cy5YtkzFGw4cPV+vWravNS01N1bFjx7Rt2zYVFxerc+fOatOmjVJSUjR27FgdO3ZMGzduVPv27dWxY8dav155ebmaNGly1vUZYyT9eAOzJJ08eVJz587VW2+9pSNHjigQCCgQCKh58+ZBxzVt2lSSgu4nAhBahB0AIXPfffdp4sSJkqSXX365xjmdOnVSu3btlJOTo+LiYqWkpEiSvF6vkpKS9NFHHyknJ0c33HDDab9W69attWfPnrOubd++fZKkpKQkSdLzzz+v+fPnKysrSz179lTz5s01efJkVVRUBB333XffSZIuvfTSs/5aABoWb2MBCJkbb7xRFRUVqqioUHp6eq3zUlNTtXHjRm3cuFGDBg1yxlNSUrRu3Tp98sknp30LS5Kuuuoqff75586KzZlkZWUpNjZWQ4YMkfTjU2A333yz7r77bvXq1UsdO3bUF198Ue24/Px8tWvXrsZVKgChwcoOgJBp1KiRs4LSqFGjWuelpqbq4YcfVmVlpbOyI/0Ydh566CGdOHHijGHn1Nthe/fuVY8ePYL2ff/99yosLFQgENCBAwf0xz/+UatXr9Zrr72mFi1aSPpxhentt99Wbm6uWrZsqczMTBUWFqpr165B59qyZYvS0tLO5a8BQAMj7AAIqbN5Oio1NVXl5eW64oorFB8f74ynpKSotLRUl19+uRISEk57jlatWmnUqFH6y1/+ooyMjKB9v/71ryVJTZo0Udu2bTVw4EB9+umnuvrqq505Tz/9tAoKCpSenq5mzZrpN7/5jW655Rb5/X5nzokTJ7Rq1SqtW7furHoHcGG4zNmu6QJAhNuzZ4+GDBmigwcPKiYmpt7P//LLL+uvf/2r1q9fX+/nBlB33LMD4KLRs2dPzZs3z3mcvL5FR0frpZdeapBzA6g7VnYAAIDVWNkBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFb7/1/x3AXJHhlHAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.hist(masses)\n",
+    "plt.ylabel('Count')\n",
+    "plt.xlabel('MW (Da)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0b0f710b-4030-4ef3-837d-bf82bef21e39",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:27.819562Z",
+     "iopub.status.busy": "2024-09-10T23:26:27.819436Z",
+     "iopub.status.idle": "2024-09-10T23:26:28.431074Z",
+     "shell.execute_reply": "2024-09-10T23:26:28.430690Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:27.819550Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "all_atoms = [atom.symbol\n",
+    "    for mol in dataset.molecules\n",
+    "    for atom in mol.atoms]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ab21f263-b5fc-4a3d-b1be-c47156477f6f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:28.431857Z",
+     "iopub.status.busy": "2024-09-10T23:26:28.431716Z",
+     "iopub.status.idle": "2024-09-10T23:26:28.511867Z",
+     "shell.execute_reply": "2024-09-10T23:26:28.511497Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:28.431844Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "all_atoms_labels, all_atoms_counts = np.unique(all_atoms,return_counts=True)\n",
+    "plt.plot()\n",
+    "plt.bar(all_atoms_labels,all_atoms_counts)\n",
+    "plt.ylabel('Number of times element appears in dataset')\n",
+    "plt.title('Total elements represented')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fbab3f42-d451-44b9-85b1-effdcc727857",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:28.512712Z",
+     "iopub.status.busy": "2024-09-10T23:26:28.512574Z",
+     "iopub.status.idle": "2024-09-10T23:26:29.186604Z",
+     "shell.execute_reply": "2024-09-10T23:26:29.186169Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:28.512698Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "atoms = []\n",
+    "for mol in dataset.molecules:\n",
+    "    atoms.extend(np.unique([atom.symbol for atom in mol.atoms]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b1a71a11-34ef-4a89-81b2-b3a5ad758ccb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T23:26:29.187210Z",
+     "iopub.status.busy": "2024-09-10T23:26:29.187092Z",
+     "iopub.status.idle": "2024-09-10T23:26:29.256076Z",
+     "shell.execute_reply": "2024-09-10T23:26:29.255729Z",
+     "shell.execute_reply.started": "2024-09-10T23:26:29.187197Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "atoms_labels,atoms_counts = np.unique(atoms,return_counts=True)\n",
+    "\n",
+    "plt.plot()\n",
+    "plt.bar(atoms_labels,atoms_counts)\n",
+    "plt.ylabel('Number of molecules with this element')\n",
+    "plt.title(\"Molecules containing each atom type\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25101e66-af8a-4064-9e0c-d1c7f6bc7266",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/input-environment.yaml b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/input-environment.yaml
new file mode 100644
index 00000000..9f1a452c
--- /dev/null
+++ b/submissions/2024-09-10-OpenFF-Iodine-Fragment-Opt-v1.0/input-environment.yaml
@@ -0,0 +1,39 @@
+name: i-frag-submit
+channels:
+  - conda-forge
+  - openeye
+dependencies:
+  - python
+  - pip
+  - click
+  - openff-toolkit
+  - openff-qcsubmit
+  - openmmforcefields
+  - openeye::openeye-toolkits
+  - openff-interchange-base
+  - qcportal
+  - jupyter
+  - seaborn
+  - pandas
+  - pydantic =2
+  - ipython
+  - ipdb
+  - pre-commit
+  - rich
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  - pytest-randomly
+  - pytest-timeout
+  - smirnoff-plugins
+  - tqdm
+  - openmm
+  - ambertools
+  - qubekit
+  - ndcctools
+  - cmiles
+  - black
+  - isort
+  - flake8 
+  - mamba
+  - nglview