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With an increasing number of new scientific papers being released, it becomesharder for researchers to be aware of recent articles in their field of study.Accurately classifying papers is a first step in the direction of personalizedcatering and easy access to research of interest. The field of DensityFunctional Theory (DFT) in particular is a good example of a methodology usedin very different studies, and interconnected disciplines, which has a verystrong community publishing many research articles. We devise a newunsupervised method for classifying publications, based on topic modeling, anduse a DFT-related selection of documents as a use case. We first create topicsfrom word analysis and clustering of the abstracts from the publications, thenattribute each publication/paper to a topic based on word similarity. We thenmake interesting observations by analyzing connections between the topics andpublishers, journals, country or year of publication. The proposed approach isgeneral, and can be applied to analyze publication and citation trends in otherareas of study, beyond the field of Density Function Theory.

Topic Modeling in Density Functional Theory on Citations of Condensed Matter Electronic Structure Packages