炔烃半加氢及氘代反应的多尺度模拟和应用数据集

本数据集由浙江工业大学化学工程学院实验室于2022至2025年间构建（核心数据采集阶段为2023年2月至2025年6月），包含三类数据：多尺度模拟方法的开发与应用、催化材料的制备与测试、ReaxFF力场及微观动力学模型。研究方法上，计算方面采用密度泛函理论（使用VASP软件与PBE泛函）、Python/MATLAB构建微观动力学模型、遗传算法优化ReaxFF力场；实验方面通过浸渍法等方法制备材料，采用XRD/XPS/SEM等技术进行表征，并利用固定床反应器等设备测试性能。数据时间精度与研究记录周期匹配，空间范围覆盖从原子尺度（键长/键角）到宏观测试尺度。数据集同时包含计算与实验数据，如反应能垒、转换频率（TOF）和材料表征结果等，以PDF、xlsx及软件说明文件等格式存储，按研究方向设置子文件夹。通过计算与实验的交叉验证以及实验参数的严格管控实现质量控制。本数据集可为催化机理研究与材料构效关系分析提供支撑，作为机器学习力场的训练数据，且部分材料具有产业化潜力，有助于解决能源与环境催化领域的问题。

This dataset was constructed by the Laboratory of the College of Chemical Engineering at Zhejiang University of Technology between 2022 and 2025, with the core data collection phase spanning from February 2023 to June 2025. It encompasses three categories of data: development and application of multi-scale simulation methods, preparation and testing of catalytic materials, and ReaxFF force fields and microkinetic models. For computational research, density functional theory (using VASP software and the PBE functional), construction of microkinetic models via Python/MATLAB, and optimization of ReaxFF force fields through genetic algorithms were adopted. For experimental work, materials were prepared via methods such as impregnation, characterized using techniques including XRD, XPS, SEM, and their performance was tested using equipment such as fixed-bed reactors. The temporal precision of the data aligns with the research recording cycle, while the spatial scope covers from the atomic scale (bond length/bond angle) to the macroscopic test scale. The dataset contains both computational and experimental data, such as reaction energy barriers, turnover frequency (TOF), and material characterization results, stored in formats including PDF, XLSX and software documentation, with subfolders organized by research direction. Quality control is realized through cross-validation between computational and experimental data and strict control of experimental parameters. This dataset can provide support for catalytic mechanism research and structure-activity relationship analysis of materials, serve as training data for machine learning force fields, and some of the materials have industrialization potential, which helps to address issues in the field of energy and environmental catalysis.

搜集汇总

背景与挑战

背景概述

该数据集由浙江工业大学化学工程学院实验室在2022至2025年间构建，聚焦于炔烃半加氢及氘代反应，包含多尺度模拟方法开发、催化材料制备与测试、ReaxFF力场及微观动力学模型三类数据，融合了计算（如密度泛函理论）和实验（如材料表征）方法。数据集旨在支持催化机理研究、材料构效关系分析和机器学习力场训练，部分材料具有产业化潜力，有助于解决能源与环境催化领域的问题。