Transition1x

<b>Transition1x - a dataset for building generalizable reactive machine learning potentials</b>https://www.nature.com/articles/s41597-022-01870-w<br>This dataset is constructed by running NEB on 10.000 reactions with H, C, N and O using the wb97x functional and 6-31G(d) basis set. This resulted in DFT calculations for 9.6 million molecular configurations on and around minimal energy paths on the potential energy surface. The data is intended for training ML models to work in transition state regions of chemical space.<br>Dataloaders and example scripts are availble in https://gitlab.com/matschreiner/T1x<br>The authors acknowledge support from the Novo Nordisk Foundation (SURE, NNF19OC0057822) and the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY2030PLUS). Ole Winther also receives support from Novo Nordisk Foundation through the Center for Basic Machine Learning Research in Life Science (NNF20OC0062606) and the Pioneer Centre for AI, DNRF grant number P1

**Transition1x——用于构建可泛化反应性机器学习势能的数据集**
https://www.nature.com/articles/s41597-022-01870-w

本数据集通过针对氢（H）、碳（C）、氮（N）与氧（O）参与的10000个反应，采用wb97x泛函与6-31G(d)基组运行nudged弹性带（NEB）方法构建而成。该过程得到了针对势能面上极小能量路径及其周边区域的960万个分子构型的密度泛函理论（DFT）计算结果。本数据集旨在用于训练可在化学空间过渡态区域发挥作用的机器学习模型。

数据加载器与示例脚本可从https://gitlab.com/matschreiner/T1x获取。

作者感谢诺和诺德基金会（SURE项目，编号NNF19OC0057822）以及欧盟“地平线2020”研究与创新计划下编号为957189（BIG-MAP）与957213（BATTERY2030PLUS）的资助协议提供的支持。Ole Winther同时获得诺和诺德基金会资助的生命科学基础机器学习研究中心（编号NNF20OC0062606）以及丹麦国家研究基金会（DNRF）编号为P1的人工智能先锋中心的支持。