Name: Ember Sikorski
Pronouns: she/her/hers
Biography:
Bio: Ember Sikorski is a Senior Member of the Technical Staff at Sandia National Laboratories in the Computational Multiscale department. She received her B.S. in Physics from University of Texas El Paso and her PhD in Materials Science and Engineering from Boise State University. She interned at Idaho National Laboratory while in grad school and worked on temperature sensors designed to endure the in-pile environment of nuclear fission test reactors. She develops machine learned interatomic potentials to study materials at the nanometer to micron scale with the accuracy of quantum methods. Her research interests include materials for low-carbon (nuclear and renewable) power in extreme environments, e.g. high temperature and irradiation. She wants to work towards a future where the demographics of the national labs represent the demographics of the nation.
Institution/Lab: Sandia National Laboratories
Website:
SRP Collaboration Topic/Title: Material Models for Fusion Energy
Field or research area: Atomistic Materials Modeling
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.)
Brief Abstract:
Achieving nuclear fusion power on earth requires incredibly durable materials capable of housing a tiny sun. We can use quantum materials modeling to make predictions about the behavior of prospective fusion materials, without the need for experiments. However, quantum models are very computationally expensive. Machine learning allows us to bypass much of the expense and enable larger scale modeling with quantum accuracy. With these models we can study thermomechanical properties and/or plasma interactions in fusion materials. Now, we can design fusion materials at the nanoscale for better performance. Project ideas include classical modeling of the fusion material, quantum modeling for the machine learning training set, or optimizing the code to better find the machine learning hyper-parameters.
Desired relevant skills, background, or interests:
Looking for students interested in applied research for nuclear energy, aerospace, or other applications with extreme environments.
Other comments:
If interested, please contact Meg McCarthy (SNL) as I will be unavailable during the matching workshop.
Do any special requirements apply? In-Person Only; U.S. Citizen Only
Other, specify:
Keywords:
Density functional theory; molecular dynamics; nuclear fusion; materials
Lightning Talk Title: Mapping Quantum Data for Large Nuclear Energy Material Simulations