DOI: 10.5281/zenodo.15801400
title: QC Optimization Dataset: OpenFF Industry Benchmark Season 1 v1.2

description: This dataset is the public counterpart of the OpenFF Industry Benchmark Season 1. Each industry partner has selected a range of diverse molecules which represent their current chemical interests. The dataset will be used in conjunction with private counterparts also designed by each partner to give an unbiased assessment of the progress and current performance of the OpenFF line of force fields in comparison with other contemporary force fields.

The v1.1 dataset features corrected Merck (MRK) molecules with explicit hydrogens. The original v1.0 dataset did not have explicit hydrogens on these molecules, resulting in poor starting conformers that have largely failed to geometry optimize under QM. The v1.1 dataset was prepared from the v1.0 dataset, excising the MRK molecules and replacing them with the explicit hydrogen variants prepared using the Season 1 protocol via openff-benchmark.

This v1.2 dataset has removed a subset of structures from v1.1 that exhibited unrealistic angles, which proved to hinder progress in converging force field parameters. The outlier records are recorded in the sage-2.2.0 repository. It also applies our standard post-submission filters.

Additional details can be found in the OpenFF QCA Dataset Submission page for this dataset.

 

General Information



Date: 2025-06-24

Name: OpenFF Industry Benchmark Season 1 v1.2

Purpose: The combination of all publicly chosen compound sets by industry partners from the OpenFF Industry Benchmark Season 1 with standard post-submission filters applied and unrealistic conformers removed.

Dataset Submitter: Jennifer A. Clark

Dataset Curator: David Dotson and Joshua Horton

Dataset Generator: David Dotson and Joshua Horton

Class: OpenFF Optimization Dataset

Dataset Type: optimization

Number of unique molecules: 9835

Number of filtered molecules: 0

Number of conformers: 74585

Number of conformers (min mean max): 1.00, 7.58, 10.00

Molecular weight (min, mean, max): 16.04, 348.58, 1105.15

Set of charges: -2.0, -1.0, 0.0, 1.0, 2.0
description: Manifest





OpenFF-Industry-Benchmark-Season-1-v1.2_optimization_view.sqlite : QCFractal view of optimization dataset 453

docker_handle_dataset_views.tar.gz : Compressed docker image providing a notebook ready to view and handle the data

README.txt on how to launch the docker image and access the data





Instructions for Dataset Handling

This data is stored a an sqlite file the is directly readable with QCFractal as a dataset view. Because sqlite is a general format, this data may also be accessed in that way, where compressed contents are python picked json strings. To make viewing and handling the data straighforward, a docker image wiith a jupyter notebook entry has been provided.

Load the docker image with:

$ docker load -i docker_handle_dataset_views.tar.gz

A user can run the docker image to spawn the jupyter notebook.



$ mkdir views; mv *sqlite views/$ mkdir outputs$ docker run -p 8888:8888 -v ./views:/workspace/views -v ./outputs:/workspace/outputs docker_handle_dataset_views


The -p flag exposed the port 8888 inside the docker image to the port by the same name externally.The -v flag exposes a directory (in this case ./views, so put your dataset views there) to a directory inside the docker image so that the jupyter notebook and access them. The ./outputs directory provides another shared directory that can be useful to pass output files.If using a M* MAC, the flag --platform=linux/amd64 could be required.

Entering the URL that starts with http://127.0.0.1:8888... in a internet browser should lead to a jupyterlab instance.