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

<b>Transition1x：用于构建可泛化反应性机器学习势能的数据集</b>https://www.nature.com/articles/s41597-022-01870-w
本数据集通过对包含氢、碳、氮、氧元素的10000个反应体系，采用wb97x泛函与6-31G(d)基组运行nudged弹性带法（NEB）构建得到。由此获得了势能面上最低能量路径及其邻近区域内共计960万个分子构型的密度泛函理论（DFT）计算数据。本数据集旨在用于训练可在化学空间过渡态区域有效运行的机器学习（Machine Learning）模型。
数据加载器与示例脚本可从以下链接获取：https://gitlab.com/matschreiner/T1x
作者感谢诺和诺德基金会（项目SURE，资助编号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（资助编号NNF20OC0062606）以及丹麦国家研究基金会（DNRF）资助编号P1的AI先锋中心支持。

搜集汇总

背景与挑战

背景概述

Transition1x数据集包含通过DFT计算生成的960万个分子构型，专门用于训练机器学习模型处理化学反应的过渡态区域。数据集支持MIT许可，并提供数据加载器和示例脚本。