Rajni Chahal

Beryllium fluoride is a key component of molten salt mixtures for next-generation nuclear reactors. Despite its importance, reliable experimental data on its thermophysical and thermochemical properties remain scarce, and predictive modeling is complicated by the slow structural relaxation of its highly viscous melt. Here, we investigate the structural stability and thermophysical behavior of BeF ₂ across solid and liquid phases using a combination of density functional theory (DFT) methods and two architectures of machine learning interatomic potentials (MLIPs). For crystalline BeF ₂ , we compare polymorph stabilities predicted by density functional theory and MLIPs, highlighting the critical role of dispersion corrections in stabilizing the α-quartz phase at 0 K and the ability of MLIPs trained exclusively on molten state configurations to accurately reproduce the relative stability of various polymorphs. MLIP-based simulations are then extended to molten BeF ₂ , whose high viscosity and network-forming character require trajectories on the order of tens of nanoseconds to obtain converged properties. Employing the hybrid PBE0-D3 functional, compared to the semilocal PBE-D3 functional, systematically improves agreement with experimental density, ionic conductivity, and heat capacity for molten BeF ₂ . Overall, this work demonstrates the potential of machine learning-driven simulations for predictive modeling of BeF ₂ across phases and provides a useful reference point for future studies of complex multicomponent fluoride systems.

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 ∼10⁴ 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.

Molten salts are critical materials for advanced energy systems, particularly in molten salt reactors (MSRs), due to their exceptional thermophysical and chemical properties. While significant progress has been made in understanding their macroscopic behaviors, detailed knowledge of their atomic structures remains limited, particularly in fluoride-based salts with high zirconium concentrations. This study investigates the atomic structure and thermophysical properties of NaF-ZrF₄ salt mixtures (53-47 and 56-44 mol %) using an integrated experimental and computational approach. X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy were employed to probe the local environment of Zr atoms across temperatures from 530 to 700 °C, revealing changes in coordination states and bond distances. Complementary ab initio molecular dynamics (AIMD) and neural network-based molecular dynamics (NNMD) simulations were validated against experimental data to elucidate short- and intermediate-range ordering in the melt. The results highlight a temperature-driven transition toward lower Zr coordination numbers and increased structural distortion, providing insights into the fluoroacidity and potential corrosiveness of these salts. This comprehensive understanding of the NaF-ZrF₄ structure supports the development of more reliable models for molten salts, aiding advancements in next-generation nuclear reactors and energy systems.

Machine learning-based interatomic potentials are used to accelerate free energy calculations in an efficient framework for computing chemical potentials. Using this framework, the melting point of lithium chloride is accurately predicted.

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl₃ molten salt across varied thermodynamic conditions (T = 473-613 K and P = 2.7-23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid-vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl₃ molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl₃ that are within 10 K (∼3%) and 0.03 g/cm³ (∼7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl₃ system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally determined molten salt structure comprising Al₂Cl₆ dimers, as validated using Raman spectra and neutron structure factor. The PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl₃ system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict temperature correlations for liquid and vapor densities in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann-Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol^-1 K^-1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

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 chemical and thermo-physical properties of molten salts. Due to its neutronic, chemical, and thermo-chemical properties, 2LiF−BeF2 is a candidate molten salt for several fusion and fission reactor designs. We perform neutron and X-ray total scattering measurements to determine the atomic structure of 2LiF−BeF2. We also perform ab-initio 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 ab-initio simulations at significantly lower computational expense while retaining near ab-initio accuracy. The comparison among experimental and modeling results at a higher resolution and efficiency than previous measurements provides the opportunity to explore the structural determination of 2LiF−BeF2 beyond the first-nearest neighbor analysis that had been previously achieved with X-ray diffraction measurements of a FLiBe melt. This work may serve as a reference for future studies of salt structure and macroscopic properties with and without the addition of solutes.