DOI: 10.5281/zenodo.15611784
title: QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.0.0

description: A quantum chemical (QC) dataset of optimization targets was generated at the OpenFF default level of theory, B3LYP-D3BJ/DZVP, and curated to train the parameters of the OpenFF 2.0.0 Sage force field. 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.

Further information can be found in the GitHub repository of these compiled datasets and the OpenFF 2.0.0 Sage force field repository.

General Information

Name: OpenFF SMIRNOFF Sage 2.0.0Purpose: Complete set of training data for OpenFF 2.0.0 SageDataset Submitter: Jennifer A. ClarkDataset Curator: Simon BoothroydDataset Generator: Jessica Maat and Hyesu JangClass: OpenFF Optimization DatasetDataset Type: optimizationNumber of unique molecules:   1039Number of filtered molecules: 0 Number of conformers:         3663Number of conformers (min mean max): 1.00, 3.53, 10.00Mean molecular weight: 261.37Max molecular weight: 544.64Set of charges: -2.0, -1.0, 0.0, 1.0Class: OpenFF TorsionDrive DatasetDataset Type: torsiondriveNumber of unique molecules: 562Number of filtered molecules: 0Number of driven torsions: 713Number of conformers: 563Number of conformers (min, mean, max): 1, 1, 2Molecular weight (min, mean, max): 46.07, 224.91, 503.42Charges: -1.0, 0.0, 1.0
description: Manifest



OpenFF-SMIRNOFF-Sage-2.0.0_optimization_view.sqlite : QCFractal view of optimization dataset 450

OpenFF-SMIRNOFF-Sage-2.0.0_torsiondrive_view.sqlite : QCFractal view of torsiondrive dataset 449

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.