2026-09-20T12:24:25Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124426 2024-09-16 col_2142_5131 col_2142_14837 com_2142_5130 com_2142_14836 com_2142_234

Zhang, Yang Vergari, Lorenzo 2024-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms The student, Luca Murg, accepted the attached license on 2024-04-27 at 21:20. The student, Luca Murg, submitted this Thesis for approval on 2024-04-27 at 21:20. This Thesis was approved for publication on 2024-04-30 at 16:57. DSpace SAF Submission Ingestion Package generated from Vireo submission #20678 on 2024-09-16 at 00:37:19 Development of transferable equivariant graph neural network forcefields for enhanced exploration of molten salt systems Murg, Luca 2024-04-30 Transferable Equivariant Graph Neural Networks Forcefields Flina Lif Naf Despite the growing interest in molten salt reactors and thermal storage systems, our understanding of the physicochemical properties of molten salts remains incomplete, partly due to challenges in performing experiments involving extreme temperatures, strict impurity control, and corrosion management, and partly due to the limited length-scale and time-scale of first-principles calculations. In this thesis, a modernized method for fabricating a transferable equivariant graph neural network forcefield for a model molten salt system using minimal DFT simulations is presented. Using this transferable machine learned forcefield, the thermal conductivity, radial distribution function, and self-intermediate scattering function of LiF-NaF was computed at various chemical ratios. Results show compelling agreement with first-principles computations, and the ability to interpolate and extrapolate various chemical ratios. Text eng https://hdl.handle.net/2142/124426 Copyright 2024 Luca Murg M.S. Thesis Nuclear, Plasma, Radiolgc Engr University of Illinois at Urbana-Champaign Nuclear, Plasma, & Rad Engr