Gaussian16 data for "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning"

This is the Gaussian 16 input and corresponding output, which was used as input into the machine learning presented in the paper titled "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning". This upload is required before submission of the paper.The 1001 and 100 snapshots from different extractions are preserved in separated directories. Each snapshot directory *_snapshot has the initial GROMACS snapshot test_*.pdb , the geometry after truncating the solvation shell in various formats, the Gaussian16 input, qsub input and the output directory *.1 with a JobID number assigned by qsub. The output directory has the standard output from Gaussian in a .log file and greped output from the .fchk file in *.out .本数据集包含高斯16（Gaussian 16）输入文件及对应输出文件，已作为输入数据用于论文《基于第一性原理理论与机器学习揭示从头设计肽ACC-二聚体的动态电子结构涨落》中所呈现的机器学习模型训练。本数据集需在论文投稿前上传。来自不同提取步骤的1001份与100份快照分别存储于独立目录中。每个名为*_snapshot的快照目录中，均包含初始格罗马克斯（GROMACS）快照文件test_*.pdb、经溶剂化层截断处理后的多种格式几何结构文件、高斯16输入文件、qsub提交脚本，以及由qsub分配作业ID（JobID）的输出目录*.1。该输出目录中包含高斯16的标准输出日志文件（.log格式），以及从.fchk文件中通过grep过滤得到的结果文件（*.out格式）。