Graph-based deep learning models for thermodynamic property prediction: The interplay between target definition, data distribution, featurization, and model architecture

This folder contains the formation energy of BDE-db, QM9, PC9, QMugs, and QMugs1.1 datasets by filtering (The training, test, and validation sets were randomly split in a ratio of 0.8, 0.1, and 0.1, respectively). The filtered process is described in the article "Graph-based deep learning models for thermodynamic property prediction: The interplay between target definition, data distribution, featurization, and model architecture" and the code can be found at https://github.com/chimie-paristech-CTM/thermo_GNN.After application of the filter procedure described in the article, final versions of the QM9 (127,007 data points), BDE-db (289,639 data points), PC9 (96,634 data points), QMugs (636,821 data points) and QMugs1.1 (70,546 data points) were obtained and used throughout this study.

本文件夹收录经筛选后的BDE-db、QM9、PC9、QMugs及QMugs1.1数据集的形成能数据，其中训练集、测试集与验证集按0.8:0.1:0.1的比例随机划分。该筛选流程的详细说明可参阅论文"Graph-based deep learning models for thermodynamic property prediction: The interplay between target definition, data distribution, featurization, and model architecture"，相关代码可访问链接https://github.com/chimie-paristech-CTM/thermo_GNN获取。经上述论文所述筛选流程处理后，本研究最终得到各数据集的正式版本：QM9（127007条数据）、BDE-db（289639条数据）、PC9（96634条数据）、QMugs（636821条数据）及QMugs1.1（70546条数据），并将其应用于本研究的全部实验环节。