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

Dataset accompanying the paper: <em>"Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields"</em>. Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. <br> <br> 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. <strong>Published in NPJ Computational Materials</strong>: https://www.nature.com/articles/s41524-023-01124-2<br> Formerly on Arxiv: https://arxiv.org/abs/2301.09931本数据集配套于论文：《催化反应路径的精确能垒：面向机器学习力场（Machine Learning Force Field）的自动化训练方案》。本数据集包含主动学习（active learning）流程中精选得到的训练集，以及用于绘制论文配图的.xyz格式文件。 该论文指出，机器学习力场的计算效率优势不仅可降低常规催化研究的计算成本，还能助力实现对催化反应路径更全面的探索。该论文已发表于《NPJ计算材料学（NPJ Computational Materials）》：https://www.nature.com/articles/s41524-023-01124-2。该论文此前曾发布于Arxiv预印本平台：https://arxiv.org/abs/2301.09931