.png){kind=icon}
返回
Machine learning the nuclear mass
.png){kind=icon}
PRE
AI
DOI：10.1007/s41365-021-00956-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background: The masses of similar to 2500 nuclei have been measured experimentally; however, >7000 isotopes are predicted to exist in the nuclear landscape from H (Z = 1) to Og (Z = 118) based on various theoretical calculations. Exploring the mass of the remaining isotopes is a popular topic in nuclear physics. Machine learning has served as a powerful tool for learning complex representations of big data in many fields. Purpose: We use Light Gradient Boosting Machine (LightGBM), which is a highly efficient machine learning algorithm, to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear masses. Methods: Several characteristic quantities (e.g., mass number and proton number) are fed into the LightGBM algorithm to mimic the patterns of the residual delta(Z,A) between the experimental binding energy and the theoretical one given by the liquid-drop model (LDM), Duflo-Zucker (DZ, also dubbed DZ28) mass model, finite-range droplet model (FRDM, also dubbed FRDM2012), as well as the Weizsacker-Skyrme (WS4) model to refine these mass models. Results: By using the experimental data of 80% of known nuclei as the training dataset, the root mean square deviations (RMSDs) between the predicted and the experimental binding energy of the remaining 20% are approximately 0.234 +/- 0.022, 0.213 +/- 0.018, 0.170 +/- 0.011, and 0.222 +/- 0.016 MeV for the LightGBM-refined LDM, DZ model, WS4 model, and FRDM, respectively. These values are approximately 90%, 65%, 40%, and 60% smaller than those of the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 was also significantly improved. The one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical models. In addition, the two-neutron separation energies of several newly measured nuclei (e.g., some isotopes of Ca, Ti, Pm, and Sm) predicted with LightGBM-refined mass models are also in good agreement with the latest experimental data. Conclusions: LightGBM can be used to refine theoretical nuclear mass models and predict the binding energy of unknown nuclei. Moreover, the correlation between the input characteristic quantities and the output can be interpreted by SHapley additive exPlanations (a popular explainable artificial intelligence tool), which may provide new insights for developing theoretical nuclear mass models.
Keyword:
Nuclear mass
Machine learning
Binding energy
Separation energy
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
2.2K
被引数:
3.4K
机构
引用论文
Evidence for an enriched mantle source for jøtunite (orthopyroxene monzodiorite) associated with the St. Urbain anorthosite, Quebec.png)
Lithos
LithosIF0
PREAI
分享收藏Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System一维周期系统的机器学习动能泛函
PREAI
分享收藏The thermal structure of Jupiter II. Observations and analysis of 8–14 micron radiationIcarus
IcarusIF0
PREAI
分享收藏
查看更多内容