Researchers from Oak Ridge National Laboratory, Cleveland Clinic, and IBM have used quantum computers to calculate nine molecular configurations of FLiBe, a material used to produce tritium fuel for fusion energy. This represents the first known instance of such computations, aiming to overcome the critical tritium supply shortage currently facing fusion power development.
Why is FLiBe essential for fusion energy production?
FLiBe is a liquid salt composed of fluorine, lithium, and beryllium. Scientists consider it a leading candidate material for extracting tritium within fusion reactors. Tritium is a rare material in nature that is necessary to produce energy in most proposed fusion machines.
Current research indicates that ensuring an adequate supply of tritium remains a significant barrier to making fusion power plants a reality. Solving this supply issue is a primary objective of the United States Department of Energy’s (DOE) Genesis Mission, which seeks to unify high-performance computing, artificial intelligence, and quantum computing to accelerate scientific discovery.
The quantum-centric techniques used to model these molten salts are the same methods currently being applied to simulate complex biological proteins containing up to 12,635 atoms.
How does quantum-centric supercomputing improve material modeling?
Classical computers often struggle to scale when modeling the atomic-level chemistry of complex materials like FLiBe. Traditional research relies on expensive physical experiments or classical computing approximation methods, which the study notes can lack accuracy.
The research team utilized “quantum-centric supercomputing,” a method that integrates central processing units (CPUs), graphics processing units (GPUs), and quantum processing units (QPUs). According to Jerry Chow, CTO of Quantum-Centric Supercomputing at IBM, this approach allows researchers to tackle scientific challenges that none of these computing paradigms can solve alone.
By using quantum circuits to solve specific parts of a problem, the team could more precisely determine the electronic structure of FLiBe. This allowed them to identify how atoms move through various configurations and how strongly they bind to tritium at a fundamental molecular level.
>“Quantum computers, such as those built by IBM and enhanced by AI and exascale computing, are key tools that accelerate the discovery and design cycles needed to produce sufficient tritium to fuel fusion reactors,” said Tom Beck, Section Head for Science Engagement in the Computing and Computational Sciences Directorate at ORNL.
What are the next steps for fusion material research?
The collaboration between ORNL, Cleveland Clinic, and IBM is ongoing. The team’s current objectives include reducing the time required for data to transfer between classical and quantum resources and scaling the size of the molecular interactions they can simulate.
The ultimate goal is to create a workflow that the broader fusion energy ecosystem can use to design and verify new materials directly. Kenneth Merz, PhD, a staff scientist at Cleveland Clinic, stated that this work extends techniques used in biological protein simulations into the field of materials science to explore fusion-relevant systems with greater accuracy and efficiency.
Watch the progress of the DOE’s Genesis Mission. It is designed to connect 17 national laboratories with advanced computing tools to solve national energy and material challenges.
Frequently Asked Questions
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What is FLiBe?
FLiBe is a molten salt mixture of fluorine, lithium, and beryllium used as a candidate material for breeding tritium in fusion reactors. -
Why is tritium so important for fusion?
Tritium is a necessary fuel for most proposed fusion power designs, but it is extremely rare in nature, making efficient production and extraction vital. -
What makes quantum-centric supercomputing different?
It combines the strengths of classical computers (CPUs and GPUs) with the specialized capabilities of quantum computers (QPUs) to solve complex chemical and physical problems that are too difficult for classical systems alone.
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