基于Condensed Matter电子结构包引文的密度泛函理论建模
Topic Modeling in Density Functional Theory on Citations of Condensed Matter Electronic Structure Packages
摘要：
随着越来越多的新科学论文被发布，研究人员越来越难以了解他们研究领域的最新文章。准确地对论文进行分类是个性化餐饮和轻松访问感兴趣的研究方向的第一步。特别是密度泛函理论（DFT）领域是一个很好的例子，它是一种用于非常不同的研究和相互关联的学科的方法，它有一个非常强大的社区发表了许多研究文章。我们设计了一种新的无监督的方法来分类出版物，基于主题建模，并使用DFT相关的文档选择作为用例。我们首先通过对出版物的摘要进行词分析和聚类来创建主题，然后根据词的相似性将每个出版物/论文归属于一个主题。然后，我们通过分析主题与出版商，期刊，国家或出版年份之间的联系进行了有趣的观察。所提出的方法是通用的，并可以应用于分析在其他领域的研究，超出了密度泛函理论领域的出版和引用趋势。
英文摘要：
With an increasing number of new scientific papers being released, it becomes harder for researchers to be aware of recent articles in their field of study. Accurately classifying papers is a first step in the direction of personalized catering and easy access to research of interest. The field of Density Functional Theory (DFT) in particular is a good example of a methodology used in very different studies, and interconnected disciplines, which has a very strong community publishing many research articles. We devise a new unsupervised method for classifying publications, based on topic modeling, and use a DFT-related selection of documents as a use case. We first create topics from word analysis and clustering of the abstracts from the publications, then attribute each publication/paper to a topic based on word similarity. We then make interesting observations by analyzing connections between the topics and publishers, journals, country or year of publication. The proposed approach is general, and can be applied to analyze publication and citation trends in other areas of study, beyond the field of Density Function Theory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
