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Ali Mohammadi Dinani

Publications and source records attributed to Ali Mohammadi Dinani.

2 recordsLinked to original sources

Ferroelectric Switching in ZnO/Zn1-xMgxO Heterostructures: Atomistic Insights into Interfacial Coupling and Layer Architecture

Ferroelectric switching in heterostructures couples composition, layer topology, temperature, and interfacial boundary conditions. ReaxFF molecular dynamics isolates these variables in ZnO/ Zn1- xMgxO/ZnO and Zn1-xMgxO/ZnO/ Zn1-xMgxO stacks. Within pristine, initially single-domain models, coupling to switchable Zn1-xMgxO reduces the applied field required to reverse ZnO by up to fivefold. Temperature generally lowers the coercive field, whereas Mg concentration produces a nonmonotonic response. At equal ZnO and Zn1-xMgxO (ZMO) proportions, structures with ZnO at the center switch at lower fields than those with ZMO at the center at all four temperatures examined, demonstrating a topology-dependent response. Layer-resolved trajectories reveal topology-dependent switching sequences with direction-dependent redistribution of normal stress near the heterointerfaces, consistent with a stress-assisted cooperative pathway. Limiting MgO-containing structures exhibit sequential multilevel switching or low-polarity trapping, depending on thickness and temperature. Fixed-charge atomistic simulations complement previous continuum descriptions by resolving structural, energetic, and local stress evolution under a common applied field. TEM and STEM-EDS observations provide experimental structural context for the modeled architectures. Together, the results establish layer topology and interfacial mechanical confinement as design variables for wurtzite ferroelectric heterostructures.

cond-mat.mtrl-sci↗

ReaxKit: A Modular Python Toolkit for Preparing, Parsing, and Analyzing ReaxFF Molecular Dynamics Simulations

Empirical reactive force field (RFF) molecular dynamics enables atomistic simulation of bond breaking, bond formation, charge redistribution, and structural evolution in chemically complex systems. The ReaxFF method is arguably the most popular and transferable of the currently available RFF methods. However, routine use of ReaxFF often requires substantial manual effort to prepare inputs, interpret engine-specific outputs, organize simulation artifacts, and develop custom analysis scripts, limiting reproducibility and scalability. Here, we present ReaxKit, a modular Python toolkit for preparing, parsing, analyzing, and managing ReaxFF molecular dynamics simulations. ReaxKit uses a separation-of-concerns architecture that distinguishes engine-specific input/output handling, canonical domain data models, scientific analysis, workflow orchestration, presentation, storage, and graphical interaction. Engine adapters convert outputs from supported simulation environments into typed, engine-independent data structures, allowing analysis modules to operate independently of native file formats. User requests are executed through consistent command-line, functional Python, and browser-based graphical interfaces, while a dedicated workspace preserves raw data, normalized datasets, analysis settings, results, logs, caches, and provenance information. Representative applications demonstrate the breadth of the toolkit, including automated generation of elastic and equation-of-state training data from Materials Project structures and mechanical properties, characterization of active sites and local structural environments, and execution of simulation campaigns through YAML-defined study workflows. These capabilities show that ReaxKit supports all essential stages of the ReaxFF workflow.

physics.chem-ph↗