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Molten salts are promising candidates in numerous clean energy applications, where challenges in experimental methods limit knowledge of their safety-critical temperature-properties correlations. Herein, we developed and employed machine learning interatomic potentials (MLIP) to study AlCl3 molten salt across varied thermodynamic conditions. The MLIP accurately predicted the existence of Al2Cl6 dimers in this molten salt as informed by Raman spectra and neutron structure factor. The MLIP is validated using available experimental data for temperature correlations with viscosities, surface tension, as well as liquid and vapor densities evaluated from two-phase coexistence simulations. In doing so, we closely predicted the critical temperature and critical density compared to reported experimental values for AlCl3. The demonstrated approach for MLIP training in closely predicting phase equilibrium in this study can be useful towards screening nuclear reactors-relevant salt compositions, helping to mitigate safety concerns., Comment: 37 pages, 9 figures

Alzheimer's Disease (AD) is a non-curable progressive neurodegenerative disorder that affects the human brain, leading to a decline in memory, cognitive abilities, and eventually, the ability to carry out daily tasks. Manual diagnosis of Alzheimer's disease from MRI images is fraught with less sensitivity and it is a very tedious process for neurologists. Therefore, there is a need for an automatic Computer Assisted Diagnosis (CAD) system, which can detect AD at early stages with higher accuracy. In this research, we have proposed a novel AD-Lite Net model (trained from scratch), that could alleviate the aforementioned problem. The novelties we bring here in this research are, (I) We have proposed a very lightweight CNN model by incorporating Depth Wise Separable Convolutional (DWSC) layers and Global Average Pooling (GAP) layers. (II) We have leveraged a ``parallel concatenation block'' (pcb), in the proposed AD-Lite Net model. This pcb consists of a Transformation layer (Tx-layer), followed by two convolutional layers, which are thereby concatenated with the original base model. This Tx-layer converts the features into very distinct kind of features, which are imperative for the Alzheimer's disease. As a consequence, the proposed AD-Lite Net model with ``parallel concatenation'' converges faster and automatically mitigates the class imbalance problem from the MRI datasets in a very generalized way. For the validity of our proposed model, we have implemented it on three different MRI datasets. Furthermore, we have combined the ADNI and AD datasets and subsequently performed a 10-fold cross-validation experiment to verify the model's generalization ability. Extensive experimental results showed that our proposed model has outperformed all the existing CNN models, and one recent trend Vision Transformer (ViT) model by a significant margin., Comment: NA

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 are indispensable to advance 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 molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIP) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures/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 Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogs with sustainable ab initio accuracy across scales.

Ion dynamics that drive the transport and thermophysical properties of molten salts are poorly understood due to challenges in precisely quantifying the spatial and temporal fluctuations of specific ions in highly disordered systems. While the Van Hove correlation function (VHF) obtained from inelastic neutron scattering (INS) probes these dynamics directly, its interpretation is limited by the inherent species-averaging of experiments, which obscures analysis of key ion transport and solvation mechanisms. Here, ab initio molecular dynamics (AIMD) is used to model the VHF, unravel its partial contributions, and elucidate its underlying ionic transport mechanisms. Slow decorrelation is revealed for oppositely charged ions (Mg2+ and Cl-) caused by ion exchange across the solvation shell between adjoining ionocovalent complexes. Furthermore, transport coefficients are accurately recovered and connections between macroscopic properties and ion dynamics are revealed. This study demonstrates the potential of ab initio-informed VHF to resolve long-standing challenges in uncovering relationships between picosecond-scale ion dynamics, mechanisms, and emergent physical properties of molten salts.

LiF-NaF-ZrF4 multicomponent molten salts are promising candidate coolants for advanced clean energy systems owing to their desirable thermophysical and transport properties. However, the complex structures enabling these properties, and their dependence on composition, is scarcely quantified due to limitations in simulating and interpreting experimental spectra of highly disordered, intermediate-ranged structures. Specifically, size-limited ab-initio simulation and accuracy-limited classical models used in the past, are unable to capture a wide range of fluctuating motifs found in the extended heterogeneous structures of liquid salt. This greatly inhibits our ability to design tailored compositions and materials. Here, accurate, efficient and transferable machine learning potentials are used to predict structures far beyond the first coordination shell in LiF-NaF-ZrF4. Neural networks trained at only eutectic compositions with 29% and 37% ZrF4 are shown to accurately simulate a wide range of compositions (11 to 40% ZrF4) with dramatically different coordination chemistries, while showing a remarkable agreement with theoretical and experimental Raman spectra. The theoretical Raman calculations further uncovered the previously unseen shift and flattening of bending band at ~250 cm-1 which validated the simulated extended-range structures as observed in compositions with higher than 29% ZrF4 content. In such cases, machine learning-based simulations capable of accessing larger time- and length-scales (beyond 17 {\AA}) were critical for accurately predicting both structure and ionic diffusivities., Comment: 7 pages, 9 figures