To assist them save time and money, scientists have started utilizing new tools provided by machine learning. Nuclear physics has experienced a flurry of machine-learning initiatives going online during the past few years, and numerous papers have been published on the topic. "It was essential to record the work that had been completed. In order for people to better comprehend the variety of activities, we genuinely wish to raise awareness of the use of ML in nuclear physics, "Amber Boehnlein remarked. Because it combines and analyses key work that has been done in the field thus far, Boehnlein believes that the essay will act as a roadmap for future studies as well as an instructional resource for people who are intrigued by the subject.

Scientists have begun turning to new tools offered by machine learning to help save time and money. In the past several years, nuclear physics has seen a flurry of machine learning projects come online, with many papers published on the subject. Now, 18 authors from 11 institutions summarize this explosion of artificial intelligence-aided work in "Machine Learning in Nuclear Physics," a paper recently published in Reviews of Modern Physics. "It was important to document the work that has been done. We really do want to raise the profile of the use of machine learning in nuclear physics to help people see the breadth of the activities," said Amber Boehnlein, lead author of the paper and the associate director for computational science and technology at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility.