Towards Understanding Low Energy Nuclear Reactions: Structuring the Literature and Applying Data Analytics

After more than three decades of research, Low Energy Nuclear Reactions (LENR) still pose a challenge to comprehensive understanding. In this study, we introduce a preliminary framework of analytical tools that could assist the LENR community in accessing literature and applying machine learning for mining insights. We first collected and structured a dataset of over 4,500 LENR publications. Following that, we designed and deployed a descriptive analytics tool to search and draw insights by using a platform that allows data slicing based on keywords, authors, publication dates, and other metadata. Additionally, we applied unsupervised machine learning algorithms to the data to generate clusters of publications based on semantics and other features. Through interactive interfaces, we enable targeted investigation of specific reported phenomena. Findings provide data-driven insights into the connections between concepts like heat, helium, transmutation, and other measured effects. The analysis also identifies key contributing authors, organizations, and publication venues. By moving beyond isolated results to higher level knowledge, this work aims to advance the field by providing revealing relationships in the collective evidence. Our preliminary results and the first version of our tools will be useful for scientists already in the LENR field, as well as for those considering research on the topic. Additionally, they will benefit other scientists and policymakers. We conclude that data science approaches show promise for demystifying and advancing LENR, and merit further application to this complex scientific challenge.