A global research team has successfully utilized artificial intelligence and machine learning to decode complex nuclear forces by analyzing data from astrophysical explosions. Published in Nature Communications, this study marks the first instance where scientists have robustly connected macroscopic astronomical observations with microscopic quantum interactions to understand how neutrons and protons behave in dense matter. The project involves physicists from Los Alamos National Laboratory and the Technical University of Darmstadt in Germany. The team leveraged multimessenger astronomy data, combining gravitational wave signals from the 2017 binary neutron star merger, known as GW170817, with X-ray emissions observed by NASA's Neutron star Interior Composition Explorer telescope. Traditionally, modeling these interactions across various scenarios is computationally intractable, often requiring hours of processing time on thousands of CPU cores for a single model. To overcome this barrier, the researchers developed an AI framework that acts as a rapid surrogate for high-fidelity calculations, linking nuclear interaction parameters directly to observable neutron star properties almost instantaneously. The AI system employs two distinct machine learning algorithms. The first integrates principles of quantum physics to rapidly solve for dense-matter properties, while the second is a neural network trained on vast datasets to map these properties to the macroscopic characteristics of neutron stars, such as size and tidal deformations. This approach allowed the team to infer constraints on nuclear couplings, which describe the strength of the strong force, without the prohibitive cost of traditional computation. Ingo Tews, a physicist at Los Alamos, noted that this work bridges the gap between the largest cosmic events and the smallest building blocks of nature. By using these AI tools, the researchers could extract data on three-body forces, one of the least understood aspects of nuclear interactions, which only become significant when three or more nucleons are in close proximity. The results from this analysis align with existing terrestrial experiments, though with larger uncertainties due to the nature of astrophysical data. Rahul Somasundaram, a co-lead author from Los Alamos, highlighted the effectiveness of the developed tools, stating they performed significantly better than anticipated. The framework not only validates current understanding but also offers a pathway to even more precise constraints as next-generation detectors come online. Future facilities, such as the Cosmic Explorer in the United States and the Einstein Telescope in Europe, will provide higher-quality data, allowing the AI models to further refine knowledge of the strong force at extreme densities. Isak Svensson from the Technical University of Darmstadt emphasized that this methodology opens a new window into strong-force physics, enabling scientists to trace the effects of fundamental interactions from neutron star observations down to the quantum level. The research also holds potential for identifying exotic forms of matter, such as phase transitions involving quarks and gluons, within the cores of these dense stellar remnants. This advancement demonstrates how AI can accelerate the resolution of long-standing physics problems by synthesizing diverse data sources from across the cosmos.