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Chemical Science|January 27, 2025
Computing chemical potentials with machine-learning-accelerated simulations to accurately predict thermodynamic properties of molten saltsLuke D Gibson, Rajni Chahal, Vyacheslav S BryantsevThe Journal of Physical Chemistry. B|January 13, 2025
Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic PotentialsRajni Chahal, Luke D Gibson, Santanu Roy, et al.JACS Au|January 2, 2023
Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF-NaF-ZrF4 Molten SaltRajni Chahal, Santanu Roy, Martin Brehm, et al.Chemical Science|March 1, 2024
Tracing mechanistic pathways and reaction kinetics toward equilibrium in reactive molten saltsLuke D Gibson, Santanu Roy, Rabi Khanal, et al.The Journal of Physical Chemistry Letters|April 25, 2024
Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed OxidesJulian Barra, Rajni Chahal, Simone Audesse, et al.ACS Applied Materials & Interfaces|July 3, 2024
Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of PolyacrylonitrileRajni Chahal, Michael D Toomey, Logan T Kearney, et al.The Journal of Physical Chemistry. B|May 1, 2025
Exploring the Local Structure of Molten NaF-ZrF4 through In Situ XANES/EXAFS and Molecular DynamicsAnubhav Wadehra, Omar Oraby, Rajni Chahal, et al.Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry|October 14, 2025
Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer LearningJulian Barra, Shayan Shahbazi, Anthony Birri, et al.Journal of the American Chemical Society|June 2, 2026
Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning PotentialsRajni Chahal-Crockett, Michael D Toomey, Logan T Kearney, et al.Pageof 1