Authors: Sean Fayfar | Rajni Chahal | H. T. Williams | D. Nathanael Gardner | Guiqiu Zheng | David Sprouster | Jörg Neuefeind | Daniel Olds | Andrea Hwang | Joanna McFarlane | Ryan Gallagher | Mark Asta | Stephen Lam | Raluca O. Scarlat | Boris Khaykovich
Source: PRX Energy (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Complex Structure of Molten FLiBe (2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>Li</mml:mi> <mml:mi mathvariant="normal">F</mml:mi> </mml:mrow> </mml:math> – <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>Be</mml:mi> <mml:mi mathvariant="normal">F</mml:mi> </mml:mrow> </mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi/> <mml:mn>2</mml:mn> </mml:msub> </mml:math> ) Examined by Experimental Neutron Scattering, X-Ray Scattering, and Deep-Neural-Network Based Molecular Dynamics The use of molten salts as coolants, fuels, and tritium breeding blankets in the next generation of fission and fusion nuclear reactors benefits from furthering the characterization of the molecular structure of molten halide salts, paving the way to predictive capability of the chemical and thermophysical properties of molten salts. Due to its neutronic, chemical, and thermochemical properties, 2 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <a:mrow> <a:mi>Li</a:mi> <a:mi mathvariant="normal">F</a:mi> </a:mrow> </a:math> - <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <e:mrow> <e:mi>Be</e:mi> <e:mi mathvariant="normal">F</e:mi> </e:mrow> </e:math> <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <i:msub> <i:mi/> <i:mn>2</i:mn> </i:msub> </i:math> is a candidate molten salt for several fusion- and fission-reactor designs. We performed neutron and x-ray total-scattering measurements to determine the atomic structure of liquid 2 <l:math xmlns:l="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <l:mrow> <l:mi>Li</l:mi> <l:mi mathvariant="normal">F</l:mi> </l:mrow> </l:math> - <p:math xmlns:p="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <p:mrow> <p:mi>Be</p:mi> <p:mi mathvariant="normal">F</p:mi> </p:mrow> </p:math> <t:math xmlns:t="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <t:msub> <t:mi/> <t:mn>2</t:mn> </t:msub> </t:math> . We also performed and neural-network molecular-dynamics simulations to predict the structure obtained by neutron- and x-ray-diffraction experiments. The use of machine learning provides improvements to the efficiency in predicting the structure at a longer length scales than is achievable with simulations at significantly lower computational expense while retaining near accuracy. We found that the NNMD simulations accurately predicted the <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <w:msubsup> <w:mrow> <w:mi>Be</w:mi> <w:mi mathvariant="normal">F</w:mi> </w:mrow> <w:mn>4</w:mn> <w:mrow> <w:mn>2</w:mn> <w:mo>−</w:mo> </w:mrow> </w:msubsup> </w:math> oligomer formations seen in the experimental first-structure-factor peak. Our combination of high-resolution measurements with large-scale molecular dynamics provided an avenue to explore and experimentally verify the intermediate-range ordering beyond the first-nearest neighbor that has posed too many experimental and computational challenges in previous works. With a deeper understanding of the salt structure and ion ordering, the evolution of salt chemistry over the lifetime of a reactor can be better predicted, which is crucial to the licensing and operation of advanced fission and fusion reactors that employ molten salts. To this end, this work will serve as a reference for future studies of salt structure and macroscopic properties with and without the addition of solutes. Published by the American Physical Society 2024 Scroll ; Computer science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Molten salt chemistry and electrochemical processes