Year: 2026
Type: preprint
Abstract: Molten salts such as FLiBe (2LiF--BeF$_2$) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate electronic ground-state energies for representative molten-salt clusters, a demanding task for correlated electronic-structure methods. Here we report the first application of heterogeneous quantum--classical computing to tritium binding in FLiBe. Clusters drawn from ab initio molecular dynamics are partitioned by an embedded-wavefunction (EWF) method into atom-centered fragments, and the largest fragments are solved on IBM quantum hardware using extended sample-based quantum diagonalization (ext-SQD). Across nine clusters, the heterogeneous quantum--classical workflow reproduces fragment ground-state energies with agreement to full configuration interaction within 0.7~kcal/mol and a mean absolute deviation of 0.3~kcal/mol. In contrast, fragmented and unfragmented conformational energy differences and tritium binding energies differ by 12~kcal/mol and 110~kcal/mol on average, respectively, identifying fragment construction rather than fragment solution as the dominant source of algorithmic bias. To the best of our knowledge, this is the first such demonstration for a charged ionic system and in particular an inorganic molten salt, where electrostatic and polarization effects make the accurate treatment of electronic correlation particularly challenging. These results also identify areas of future research towards an accurate and scalable quantum--classical workflow to compute free-energy estimates of tritium speciation in fusion blankets.
Source: arXiv (Cornell University)
Authors: Das, Susanta, Santos, Thiago J. Pinheiro Dos, Bhowmik, Subhamoy, Bazayeva, Milana, Li, Zhen, Shajan, Akhil, Kaliakin, Danil, Liang, Fangchun, Bryantsev, Vyacheslav S., Geist, Al, Gomez, Abigail McClain, Pellegrini, Thaddeus, Walkup, Robert, Seelam, Seetharami R., Motta, Mario, Merz,, Kenneth M., Jr., Beck, Thomas
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Cleveland Clinic, Cleveland Clinic Foundation, Michigan State University, National Energy Research Scientific Computing Center, Office of Science, College of Engineering, Michigan State University, Basic Energy Sciences, Advanced Scientific Computing Research, Oak Ridge National Laboratory
Awards: G1019100964, G121915436, G1645119126, G203625172, G2411683797, G2988778103, G3945850812, G4370606643, G4423657506, Collaborative Research: Frameworks: Interoperable High-Performance Classical, Machine Learning and Quantum Free Energy Methods in AMBER, G7651947074, G807938293, G8269158468, G8906985441
OpenAlex ID: W7166738196
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