摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-研究人员详细介绍了机器学习的新数据。根据《新闻周刊》编辑在南通的新闻报道,研究表明:“金属-有机骨架(MOF)材料具有比表面积高、孔容大、组织结构可调等优点,在储气、吸附分离、催化等领域受到广泛关注。”国家自然科学基金项目(NSFC)、浙江省自然科学基金项目。我国新闻记者从南通大学的一项研究中得到一句话:“近年来,MOFs的数量呈爆炸性增长趋势,而作为人工智能的一个分支,其强大的适应性、规模性、可扩展性和可扩展性。”机器学习(ML)为综合评价多孔材料在各种场景下的应用性能提供了有力的工具,弥补了传统多孔材料制备和设计中Hazar DS复杂、耗时和安全的缺点,并利用线性回归、随机森林和神经网络等ML算法建立了多孔材料模型。它能够预测具有吸附性能、电性能、催化性能、机械性能和热力学性能的高性能MOF。它促进了ML和MOF的联合发展。本文综述了ML辅助MOF设计的一般实现方法和过程,包括数据收集、特征选择、算法设计和评价。此外,还介绍了ML辅助MOF设计的基本原理。"综述了ML的经典算法及其在mof分类预测中的应用."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Nantong, People's Republic of Chin a, by NewsRx editors, research stated, "Metal-organic framework (MOF) materials have the advantages of high specific surface area, large pore volume and adjusta ble organizational structure. It has received widespread attention in gas storag e, adsorption separation, catalysis and other fields." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Zhejiang Province. Our news journalists obtained a quote from the research from Nantong University, "The quantity of MOFs has shown an explosive growth trend in recent years. In a ddition, as a branch of artificial intelligence, the powerful adaptability, scal ability, and automation of machine learning (ML) provide a powerful tool for com prehensively evaluating the application performance of MOFs in various scenarios . This makes up for the shortcomings of complex, time-consuming and safety hazar ds in the preparation and design of traditional porous materials. By building mo dels using ML algorithms such as linear regression, random forests, and neural n etworks, it is able to predict high-performance MOFs with adsorption properties, electrical properties, catalytic properties, mechanical properties, and thermod ynamics. It promotes the joint development of ML and MOFs. This review provides an overview of the general implementation methods and processes for ML assisted MOF design, including data collection, feature selection, algorithm design, and evaluation. In addition, a summary of the classic algorithms of ML and their app lications in the classification and prediction for MOFs are summarized."
NETL
NSTL