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Webinar
Predictive Multiscale Simulation for Advanced Materials: From Machine-Learned Atomistics to Microstructure Evolution

Presented by Dr. Kyle Starkey
July 1st, 2nd, 3rd
Live Q&A Sessions. Please choose a day and time that works for your schedule. The one hour webinar is repeated on various days and times to fit schedules worldwide.
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Nuclear materials in fission and fusion reactors degrade through the same process: chemistry and microstructure evolving together across scales, under conditions no single experiment can reproduce. Autoclave and irradiation campaigns run for years and demand hot-cell handling of radioactive, tritiated samples, yet cannot recreate the combined neutron, thermal, and chemical loads of service.
This webinar presents a single, parameter-free simulation workflow that runs from density functional theory to machine-learned interatomic potentials to continuum phase-field modeling, drawing all parameters from atomistic calculations rather than empirical fits. Through case studies in zirconium cladding corrosion, hydride formation and fracture, tritium trapping in oxidized tungsten, and radiation damage, I will show how one atomistics-to-continuum pipeline built on MedeA moves materials qualification toward predictive, component-lifetime simulation.
In this webinar, you will learn how to:
• Build a parameter-free DFT → MLP → phase-field workflow for complex materials
• Predict and validate microstructure with no fitting to experimental data
• Extend first-principles accuracy to large-scale, finite-temperature simulation
• Model coupled chemistry, transport, and microstructure evolution in MedeA PhaseField
• Apply one workflow across fission and fusion materials problems
Who should attend:
• Materials scientists and computational chemists in industry and academia
• R&D engineers in nuclear, structural, or functional materials
• Researchers modeling corrosion, hydrogen/tritium transport, or radiation damage
• DFT users seeking scalable, multiscale extensions
• Teams advancing predictive materials qualification and lifetime prediction
