Published January 24, 2023 | Version v0
Dataset Open

Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields

  • 1. University of Cambridge
  • 2. BASF SE

Description

Dataset accompanying the paper: "Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields". Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. 

The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways.

Published in NPJ Computational Materialshttps://www.nature.com/articles/s41524-023-01124-2
Formerly on Arxiv: https://arxiv.org/abs/2301.09931

Files

figures.zip

Files (8.9 MB)

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md5:7c0a4df010c4779c9c631ef535b262d8
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Additional details

Funding

UK Research and Innovation
EPSRC Centre for Doctoral Training in Automated Chemical Synthesis Enabled by Digital Molecular Technologies EP/S024220/1
UK Research and Innovation
The UK Car-Parrinello HEC Consortium EP/X035891/1
UK Research and Innovation
Support for the UKCP consortium EP/P022065/1