Advancing Nuclear Physics with Machine Learning and Artificial Intelligence
Abstract
Machine learning (ML) and artificial intelligence (AI) are becoming powerful tools in scientific research across various disciplines. In this review, we summarize recent progress in AI-assisted studies of nuclear structure and reaction observables, heavy-ion collisions and dense nuclear matter properties, many-body wave functions, experimental facilities and data analysis. This review focus on new progress since the last review in 2023, and machine learning in nuclear physics is evolving from purely data inferences to physics informed learning. Future directions on the integration of physical knowledge with modern learning architectures, large foundation models and other emerging methods are also reviewed. These developments suggest that AI is enabling and advancing new approaches towards most challenging nuclear physics problems.
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BibTeX
@misc{he2026advancing,
title = {{Advancing Nuclear Physics with Machine Learning and Artificial Intelligence}},
author = {Wanbing He and Qingfeng Li and Yugang Ma and Zhongming Niu and Junchen Pei and Yingxun Zhang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.38711},
eprint = {2609.38711},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.38711},
}
RIS
TY - UNPB
TI - Advancing Nuclear Physics with Machine Learning and Artificial Intelligence
AU - Wanbing He
AU - Qingfeng Li
AU - Yugang Ma
AU - Zhongming Niu
AU - Junchen Pei
AU - Yingxun Zhang
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.38711
UR - https://arxiv.org/abs/2609.38711
AB - Machine learning (ML) and artificial intelligence (AI) are becoming powerful tools in scientific research across various disciplines. In this review, we summarize recent progress in AI-assisted studies of nuclear structure and reaction observables, heavy-ion collisions and dense nuclear matter properties, many-body wave functions, experimental facilities and data analysis. This review focus on new progress since the last review in 2023, and machine learning in nuclear physics is evolving from purely data inferences to physics informed learning. Future directions on the integration of physical knowledge with modern learning architectures, large foundation models and other emerging methods are also reviewed. These developments suggest that AI is enabling and advancing new approaches towards most challenging nuclear physics problems.
ER -