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Rajni Chahal-Crockett

Publications and source records attributed to Rajni Chahal-Crockett.

2 recordsLinked to original sources

Evaluating dispersion models for ab initio simulation of G-I and G-II molten fluoride salts

Ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is a powerful approach for modeling molten salts. However, standard exchange-correlation functionals often neglect dispersion interactions, introducing potential errors in property predictions. Dispersion corrections are commonly applied ad hoc to match experimental salt densities, but their systematic impact on predicting structure, thermophysical, and transport properties of salt remains unexamined. This study evaluates the impact of Grimme's DFT-D and nonlocal van der Waals (vdW-DF) corrections on molten fluorides of Group-I (LiF, NaF, KF) and Group-II (BeF$_2$, MgF$_2$, CaF$_2$), which are relevant to reactor applications. Results indicate that dispersion corrections have a minor effect on binding energies but significantly influence density predictions. Systematic benchmarking across compositions and temperatures reveals that semi-empirical dispersion models often produce more accurate densities compared to vdW-DF. Diffusion coefficients remain largely invariant to dispersion corrections at fixed densities, while coordination number distributions exhibit notable differences based on chosen dispersion. BeF$_2$, in particular, deviates from other fluorides, showing pronounced structural and dynamical differences in the absence of dispersion corrections. This highlights the necessity of dispersion effects for high-charge-density cations that promote intermediate- to long-range ordering. These findings provide a systematic framework for selecting dispersion models in molten salt simulations, improving density and structural predictions.

cond-mat.mtrl-sci↗

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH- dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li+-coupled electron transfer along the PAN backbone, causing ~10,000 times faster sequential ring-formation of the remaining nitriles. PAN's extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

cond-mat.mtrl-sci↗