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CONCEPTUAL ANALYSIS article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1401782

Exploring Artificial Intelligence Techniques to Research Low Energy Nuclear Reactions, a Type of Nuclear Energy

Provisionally accepted
Anasse Bari Anasse Bari 1*Tanya Garg Tanya Garg 1*Yvonne Wu Yvonne Wu 1Sneha Singh Sneha Singh 1David Nagel David Nagel 2
  • 1 New York University, New York City, New York, United States
  • 2 George Washington University, Washington, D.C., District of Columbia, United States

The final, formatted version of the article will be published soon.

    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 has the potential to be 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 open up 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 studies, along with a user-friendly web interface for widespread adoption and utilization. The findings contribute to the understanding and progression of LENR research through data-driven analysis and tool development, enabling more informed decision-making and strategic planning for future research in this field. The insights derived from this study, along with the experimental tools we developed and deployed, hold the potential to significantly aid researchers in advancing their studies in LENR, a form of nuclear energy 1

    Keywords: Low energy nuclear reactions (LENR), Topic Modeling, predictive analytics, text classification, unsupervised learning

    Received: 15 Mar 2024; Accepted: 13 Jun 2024.

    Copyright: © 2024 Bari, Garg, Wu, Singh and Nagel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Anasse Bari, New York University, New York City, 10012, New York, United States
    Tanya Garg, New York University, New York City, 10012, New York, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.