DOI: 10.5281/zenodo.15635099
title: QC Fitting Datasets for OpenFF SMIRNOFF Sage 2.2.0

description: Description

 

A quantum chemical (QC) optimization and torsiondrive datasets generated at the OpenFF default level of theory, B3LYP-D3BJ/DZVP, and curated to train parameters in OpenFF 2.2.0 Sage with improved small ring internal angles and sulfamide geometries.

 

Additional details can be found in the GitHub dataset repository and the Force field repository.



General Information




* Date: 2025-05-23

* Purpose: Complete set of training data for OpenFF 2.2.0 Sage

* Name: OpenFF SMIRNOFF Sage 2.2.0

* Dataset submitter: Jennifer A Clark

* Dataset curator: Pavan Behara

 

* Class: OpenFF Optimization Dataset

* Dataset Type: optimization

* Number of unique molecules: 1691

* Number of filtered molecules: 0

* Number of conformers: 5126

* Number of conformers (min, mean, max): 1.00, 3.03, 12.00

* Molecular weight (min, mean, max): 16.04, 236.01, 544.64

* Charges: -3.0, -2.0, -1.0, 0.0, 1.0, 2.0

* Dataset generator: Chaya Stern, Hyesu Jang, Jessica Maat, and Pavan Behara

 

* Class: OpenFF TorsionDrive Dataset

* Dataset Type: torsiondrive

* Number of unique molecules: 956

* Number of filtered molecules: 0

* Number of driven torsions: 1290

* Number of conformers: 982

* Number of conformers (min, mean, max): 1, 1, 3

* Molecular weight (min, mean, max): 46.07, 185.48, 503.42

* Charges: -1.0, 0.0, 1.0

* Dataset generator: Simon Boothroyd, John Chodera, Trevor Gokey, Hyesu Jang, Yudong Qiu, Bryon Tjanaka
description: Manifest





OpenFF-SMIRNOFF-Sage-2.2.0_optimization_view.sqlite : QCFractal view of optimization dataset 447

OpenFF-SMIRNOFF-Sage-2.2.0_torsiondrive_view.sqlite : QCFractal view of torsiondrive dataset 448

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.