About

Massimiliano Fratoni is Xenel Distinguished Associate Professor in the Department of Nuclear Engineering at the University of California, Berkeley. He received a Laurea in Nuclear Engineering from Sapienza Università di Roma, and a MSc and a PhD from the University of California, Berkeley. Prior to joining UC Berkeley, he held a Research Scientist position at Lawrence Livermore National Laboratory and a faculty position at The Pennsylvania State University. His research focuses on solving engineering challenges related to developing and deploying advanced reactors and advanced fuel cycles. He has contributed to the design and development of virtually all Generation IV reactor concepts and beyond, with expertise spanning solid- and liquid-fueled molten salt reactors, sodium- and lead-cooled fast reactors, reactors for space applications, accident-tolerant fuels, fusion reactors, waste repository performance, and applications of machine learning for nuclear data and reactor operation.

Research topics

• Mathematics
• Engineering
• Thermodynamics
• Mechanics
• Geology
• Nuclear engineering
• Physics
• Nuclear physics

Selected publications

• High-Fidelity Deterministic Solver for Pebble Bed Reactors

  Transactions of the American Nuclear Society · 2025-01-01

  articleSenior author
  PublisherDOI

• A Reactor Transient Benchmark for Molten Salt Reactor Experiment Pump Transient Tests

  Nuclear Science and Engineering · 2025-05-05 · 3 citations

  article
  PublisherDOI

• Multiphysics Fuel Performance Modeling of TRISO-Bearing Fuel in Advanced Reactor Environments

  Nuclear Science and Engineering · 2025-11-17

  articleOpen access
  PublisherDOI

• Thermo-mechanical property correlations for uranium mononitride with uncertainty quantification

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Safety Analysis on Fission Induced Thermophysical Property Alterations of MCFR Fuel Salt

  Transactions of the American Nuclear Society · 2025-01-01

  article
  PublisherDOI

• Burnup Measurement using Bent Crystal Diffraction Spectrometers for Pebble Bed Reactors

  arXiv (Cornell University) · 2025-10-09

  preprintOpen accessSenior author

  Burnup measurement is essential for monitoring and controlling pebble bed reactors (PBRs), where fuel pebbles circulate rapidly through the core. However, conventional gamma spectroscopy using high purity germanium (HPGe) detectors is difficult due to high activity levels in discharge pebbles, leading to excessive dead time and Compton scattering. This study explores the use of bent crystal diffraction (BCD) spectrometers to filter the emitted gamma spectrum and isolate key peaks for improved measurement accuracy and speed. Pebble wise depletion calculations were performed and the resulting spectra were analyzed using ray tracing (SHADOW3) and gamma response modeling (GADRAS). Key isotopes, $^{137m}$Ba/$^{137}$Cs, $^{239}$Pu, $^{144}$Ce, $^{148m}$Pm, and $^{140}$La, were found to strongly correlate with burnup, residence time, core passes, plutonium production, and fluence. Machine learning regression models applied to simulated spectra achieved a coefficient of determination ($R^2$) as high as 0.995 for burnup prediction. Among various BCD configurations, mosaic silicon crystals in the (440) orientation combined with an HPGe detector provided optimal performance for $^{137m}$Ba, while (220) and (440) configurations paired with scintillators were effective for the remaining isotopes.

  PublisherOA PDFDOI

• First Results for Uranium Nitride Fuel Characterization Using an Accelerated Fuel Qualification Process

  Nuclear Science and Engineering · 2025-10-27

  article
  PublisherDOI

• Verification and Validation of Depletion Calculations for TRISO Fuel Against Data from the Second Advanced Gas Reactor Irradiation Campaign

  Nuclear Technology · 2025-02-12

  articleSenior author
  PublisherDOI

• Uncertainty Quantification of Cl-35 Nuclear Data on a Molten Chloride Fast Reactor

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Machine learning models for volumetric swelling in uranium nitride

  Journal of Nuclear Materials · 2025-06-18 · 2 citations

  article
  PublisherDOI

Frequent coauthors

• E. Greenspan

  University of California, Berkeley

  24 shared

• Yves Robert

  University of Tennessee at Knoxville

  14 shared

• Manuele Aufiero

  13 shared

• Tatiana Siaraferas

  11 shared

• Nader Satvat

  10 shared

• Jeffrey J. Powers

  9 shared

• Chris Keckler

  University of California, Berkeley

  9 shared

• Brandon Haugh

  9 shared

Education

• PhD, Nuclear Engineering

  University of California Berkeley

  2008

• MS, Nuclear Engineering

  University of California Berkeley

  2007

• Laurea, Nuclear Engineering

  Universita degli Studi di Roma La Sapienza

  2004

Awards & honors

• Untermyer & Cisler Reactor Technology Medal, ANS 2024
• Xenel Distinguished Professor, 2020-2025 & 2025-2030
• Early Career Reactor Physicist Award, ANS 2018
• Reactor Physics Division Best Paper Award, ANS Annual Meetin…
• Innovations in Nuclear Technology R&D Award – Michael Martin…