arXiv subjects

Harender S. Dhattarwal

Publications and source records attributed to Harender S. Dhattarwal.

6 recordsLinked to original sources

arXiv2026

Dielectric Response of Short and Long Range Models of Nonuniform Liquids and Implications for Machine Learned Interatomic Potentials

Dielectric screening of electric fields is modified by interfaces and confinement, and any accurate description of interfacial chemistry necessitates properly modeling the nonuniform dielectric tensor. However, models with short range interactions, such as local machine learned interatomic potentials (MLIPs), are increasingly used in simulations of molecular interfaces without quantitative understanding of their nonuniform dielectric response. To build this understanding, we use the framework provided by local molecular field (LMF) theory to quantify the roles of short and long range electrostatics in confined water. Short and long range models predict the same transverse dielectric profile but short range models predict qualitatively different longitudinal dielectric profiles than their long range counterparts. To understand these differences, we develop a Landau theory that shows that long range interactions stiffen polarization fluctuations by a factor of the bulk dielectric constant and reduce the interfacial polarization correlation length by an order of magnitude. LMF theory, which captures long range interactions with an averaged field, can correct the structure and dielectric response, but the standard fluctuation relation no longer holds. We derive a fluctuation relation and integral equation for LMF theory and demonstrate their accuracy for predicting longitudinal dielectric response. MLIPs that incorporate interfacial configurations in their training essentially learn a static mean field correction at the interface, such that the integral equation can be used to predict their nonuniform dielectric profile from polarization fluctuations. We also show that short range MLIPs can be combined with symmetry preserving mean field (SPMF) theory to provide a linear-scaling alternative for long range interactions and predicting nonuniform dielectric functions.

physics.chem-ph↗

arXiv2026

Oxygen vacancies beyond the dilute limit in doped CaMnO3 perovskites and implications for screening materials in thermochemical applications

Thermochemical energy storage (TCES) in oxide perovskites relies on reversible oxygen vacancy formation, and computational high-throughput screening of candidate materials has predominantly used the single oxygen vacancy formation energy (OVFE) as the key descriptor. We demonstrate that the OVFE is insufficient for screening cubic CaMnO3 perovskites, because the stoichiometric compound is not the minimum energy reference state; vacancies are inherently present at operating temperatures. Materials with negative single OVFEs are routinely excluded from screening datasets as unsuitable, but this reflects a mischoice of reference state rather than a genuine materials limitation, and risks discarding promising TCES candidates. We address this by computing OVFEs as a function of vacancy concentration using ab initio density functional theory, establishing the equilibrium vacancy concentration as the correct reference point. OVFE curves referenced to this minimum align with experimentally measured reduction enthalpies, providing a framework directly comparable to experiments. We further show that A-site and B-site doping modify the vacancy formation landscape through distinct mechanisms. A-site dopants act primarily through strain relaxation and symmetry breaking, while B-site dopants reshape the local redox environment and introduce strong configurational dependence. Finally, we develop a thermodynamic model incorporating configurational entropy that accurately predicts equilibrium oxygen stoichiometry as a function of temperature and oxygen partial pressure and reveals that selective reduction of Mn4+ versus B-site dopant ions can tune the onset temperature for vacancy formation. These results establish a screening framework for perovskite TCES materials and provide practical guidance for extending high-throughput workflows beyond the single-vacancy paradigm.

cond-mat.mtrl-sci↗

arXiv2025

Electronic Fluctuations and Ionic Dynamics in Molten Silver Iodide

Molten salts are high-temperature ionic liquids whose unique combination of strong Coulombic interactions, large polarizabilities, and high ionic conductivities makes them central to energy storage, metallurgy, and nuclear technology. Understanding their delicate balance of Coulomb forces, short-range repulsion, and electronic polarization, particularly regarding the role that electronic fluctuations play in their structure and dynamics, is critical to predictively designing molten salts for applications of interest. We investigate the importance of electronic fluctuations in molten AgI using density functional theory, a universal machine learning model (Orb), and a classical, empirical pairwise model of interionic interactions. We find that directional polarization fluctuations of iodide ions enhance Ag+ diffusion, manifesting as enhanced force fluctuations and structure in the time-dependent friction experienced by the cations. The coupling between iodide polarization fluctuations and silver diffusion creates a dynamic asymmetry; Ag+ motion is tightly linked to the instantaneous polarization of neighboring I-, whereas I- dynamics are relatively unperturbed by electronic fluctuations. For all structural and dynamic quantities investigated, the Orb model is in excellent agreement with density functional theory-based simulations, highlighting the ability of this universal neural network potential to capture many-body polarization effects. In contrast, the empirical force field fails to reproduce key structural and dynamic quantities involving cations, ultimately because it neglects dynamic electronic fluctuations. Our findings connect liquid=state ionic dynamics with the "electronic paddle-wheel" mechanism of ionic diffusion in superionic solids and motivate further exploration of polarization fluctuation effects in complex electrolytes and ionic liquids.

physics.chem-ph↗

arXiv2025

Electronic Paddlewheels Impact the Dynamics of Superionic Conduction in AgI

Solid-state ion conductors hold promise as next generation battery materials. To realize their full potential, an understanding of atomic-scale ion conduction mechanisms is needed, including ionic and electronic degrees of freedom. Molecular simulations can create such an understanding, however, including a description of electronic structure necessitates computationally expensive methods that limit their application to small scales. We examine an alternative approach, in which neural network models are used to efficiently sample ionic configurations and dynamics at ab initio accuracy. Then, these configurations are used to determine electronic properties in a post-processing step. We demonstrate this approach by modeling the superionic phase of AgI, in which cation diffusion is coupled to rotational motion of local electron density on the surrounding iodide ions, termed electronic paddlewheels. The neural network potential can capture the many-body effects of electronic paddlewheels on ionic dynamics, but classical force field models cannot. Through an analysis rooted the generalized Langevin equation framework, we find that electronic paddlewheels have a significant impact on the time-dependent friction experienced by a mobile cation. Our approach will enable investigations of electronic fluctuations in materials on large length and time scales, and ultimately the control of ion dynamics through electronic paddlewheels.

cond-mat.mtrl-sci↗

arXiv2023

Electronic paddle-wheels in a solid-state electrolyte

Solid-state superionic conductors (SSICs) are promising alternatives to liquid electrolytes in batteries and other energy storage technologies. The rational design of SSICs and ultimately their deployment in battery technologies is hindered by the lack of a thorough understanding of their ion conduction mechanisms. In SSICs containing molecular ions, rotational dynamics couple to translational diffusion to create a 'paddle-wheel' effect that facilitates conduction. The paddle-wheel mechanism explains many important features of molecular SSICs, but an explanation for ion conduction and anharmonic lattice dynamics in SSICs composed of monatomic ions is still needed. We predict that ion conduction in the classic SSIC AgI involves 'electronic paddle-wheels,' rotational motion of lone pairs that couple to and facilitate ion diffusion. The electronic paddle-wheel mechanism creates a universal perspective for understanding ion conductivity in both monatomic and molecular SSICs that will create design principles for engineering solid-state electrolytes from the electronic level up to the macroscale.

physics.chem-ph↗

arXiv2023

Dielectric Saturation in Water from a Long Range Machine Learning Model

Machine learning-based neural network potentials have the ability to provide ab initio-level predictions while reaching large length and time scales often limited to empirical force fields. Traditionally, neural network potentials rely on a local description of atomic environments to achieve this scalability. These local descriptions result in short range models that neglect long range interactions necessary for processes like dielectric screening in polar liquids. Several approaches to including long range electrostatic interactions within neural network models have appeared recently, and here we investigate the transferability of one such model, the self consistent neural network (SCFNN), which focuses on learning the physics associated with long range response. By learning the essential physics, one can expect that such a neural network model should exhibit at least partial transferability. We illustrate this transferability by modeling dielectric saturation in a SCFNN model of water. We show that the SCFNN model can predict non-linear response at high electric fields, including saturation of the dielectric constant, without training the model on these high field strengths and the resulting liquid configurations. We then use these simulations to examine the nuclear and electronic structure changes underlying dielectric saturation. Our results suggest that neural network models can exhibit transferability beyond the linear response regime and make genuine predictions when the relevant physics is properly learned.

physics.chem-ph↗