The AI/ML for particle accelerators living review

LIVING REVIEW • CONTINUOUSLY UPDATED 🕒 Last update: January 13, 2026, 04:08 UTC 🔮 Next update: January 14, 2026, 04:08 UTC
367+
Papers Reviewed
16
Categories
Jan 2026
Last Updated
13
Papers This Month

Overview

This living review provides a comprehensive survey of artificial intelligence and machine learning applications in particle accelerator science. As the field rapidly evolves, we continuously update this review to reflect the latest research, methodologies, and experimental results from facilities worldwide. Our goal is to serve as a central reference for researchers, operators, and engineers working at the intersection of AI/ML and accelerator physics.

Latest Additions

Recently added papers from January 2026

Machine Learning Based Efficiency Calculator (MaLBEC) for Nuclear Fusion Diagnostics
Kimberley Lennon, Chantal Shand, Gemma Wilson, et al. • Journal of Fusion Energy (2026)
Evaluation of the accuracy of detecting C-shaped canals in mandibular second molars identified by cone-beam computed tomography on panoramic radiographs using artificial intelligence algorithms developed with deep learning methods
O. Uysal, M. Polat, H. M. Akgül • Oral Radiology (2026)
Machine Learning-Based Prediction of Weld Pool Features in Laser-Arc Hybrid Welding of Ultra-High-Strength Wear-Resistant Steels
Bin Zhang, Zhe Gao, Sitong Li, et al. • Journal of Materials Engineering and Performance (2026)
A physics-informed machine learning framework for unified prediction of superconducting transition temperatures
Ehsan Alibagheri, Mohammad Sandoghchi, Alireza Seyfi, et al. • Materials Today Physics (2026)
Evaluating machine learned nuclear data precision in full core nuclear reactor Monte Carlo neutronics and computational efficiency analyses
Alexander Hashemi, Rafael Macián-Juan, Martin Ohlerich • Scientific Reports (2026)
View All Papers →