The world urgently needs new sources of clean energy due to a growing global population, rising energy use, and the effects of climate change. Nuclear energy is one of the most promising solutions for meeting the world’s energy needs now and in the future. One type of nuclear energy, Low Energy Nuclear Reactions (LENR), has gained interest as a potential clean energy source. Recent AI advancements create new ways to help research LENR and to comprehensively analyze the relationships between experimental parameters, materials, and outcomes across diverse LENR research endeavors worldwide. This study explores and investigates the effectiveness of modern AI capabilities leveraging embedding models and topic modeling techniques, including Latent Dirichlet Allocation (LDA), BERTopic, and Top2Vec, in elucidating the underlying structure and prevalent themes within a large LENR research corpus. These methodologies offer unique perspectives on understanding relationships and trends within the LENR research landscape, thereby facilitating advancements in this crucial energy research area. Furthermore, the study presents LENRsim, an experimental machine learning tool to identify similar LENR
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