DOI: 10.5281/zenodo.15801338
title: QC Singlepoint Dataset: MLPepper RECAP Optimized Fragments v1.1

description: A single point dataset created by combining the 50k ESP from Simon and Br substituted set from Lily.

This was then extended to cover Boron and Silicon by applying the RECAP decomposition scheme in RDKit to the B & Si pubchem set from SPICE2. This dataset now contains additional iodine containing molecules. Each fragment had 5 conformations generated which were optimised locally using an AIMNET2 model trained to wb97m-d3. 

The aim of the dataset is to provide polarised and gas phase electrostatic properties which can be used to generate ML models for partial charge prediction. Unlike past datasets the wavefunction will not be saved to recompute the ESP instead we recommend building the ESP from the MBIS atomic multipoles which save substantial amount of space. 

An off equilibrium data set will also be generated to enable conformation dependent prediction of charges.

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

General Information

* Date: 2025-07-01* Class: OpenFF SinglePoint Dataset* Purpose: Single point property calculations for charge models, expanded to include iodine* Name: MLPepper RECAP Optimized Fragments v1.1* Number of unique molecules: 56351* Number of conformers: 75097* Number of conformers (min, mean, max): 1.00, 1.33, 5.00* Molecular weight (min, mean, max): 32.12, 163.20, 701.59* Charges: -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0* Dataset submitter: Jennifer A Clark* Dataset curator: Josh Horton / Charles Adams* Dataset generator: Josh Horton / Charles Adams
description: Manifest





MLPepper-RECAP-Optimized-Fragments-v1.1_singlepoint_view.sqlite : QCFractal view of singlepoint dataset 452

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.