arXiv subjects

Seyed Mahdi Mastoor

Publications and source records attributed to Seyed Mahdi Mastoor.

3 recordsLinked to original sources

arXiv2026

Multilayer Screening of Double and Conventional Perovskite Solar Cells Using SCAPS-1D and Machine Learning: Optimization of ETL, HTL, and Absorber for High-Efficiency Architectures

The combinatorial design space of multilayer perovskite solar cells is vast, yet exhaustive experimental or computational screening of all possible material combinations remains impractical. Here, we integrate SCAPS-1D device simulations with machine learning to systematically explore 125 device architectures constructed from five electron transport layers (ETL), five absorbers (including lead-free double perovskites), and five hole transport layers (HTL). A representative subset of configurations is used to train a machine learning (ML) model, which predicts the power conversion efficiency (PCE) of the remaining unexplored structures. Leave-One-Group-Out cross-validation yields a Spearman rank correlation, demonstrating reliable ranking capability. SHAP (SHapley Additive exPlanations) analysis reveals that the HTL band gap, absorber band gap, and ETL electron affinity are the most influential descriptors, providing physical insights into interfacial recombination and charge extraction. The machine learning model identifies several high-performance configurations that are subsequently verified by full SCAPS-1D simulations. Among them, the device FTO/TiO$_2$/Cs$_2$AgBiBr$_6$/NiO/Ag achieves a PCE of 28.85%, and the ML-suggested structure FTO/SnO$_2$/Cs$_2$AgInBr$_6$/NiO/Ag exhibits 28.62%, outperforming a closely related literature architecture by approximately 4% absolute. Notably, eight of the top-11 structures employ the lead-free double perovskite Cs$_2$AgInBr$_6$. This work demonstrates that a physics-based, data-driven workflow combining SCAPS-1D, ML, and SHAP can accelerate the discovery of high-efficiency, environmentally friendly perovskite solar cells while providing transparent design rules. The approach is generalizable to other multilayer optoelectronic systems.

cond-mat.mtrl-sci↗

arXiv2025

Scalable Machine Learning Models for Predicting Quantum Transport in Disordered 2D Hexagonal Materials

We introduce scalable machine learning models to accurately predict two key quantum transport properties, the transmission coefficient T(E) and average local density of states (Average-LDOS) in two-dimensional (2D) hexagonal materials with magnetic disorder. Using a tight binding Hamiltonian combined with the Non-Equilibrium Green's Function (NEGF) formalism, we generate a large dataset of over 400,000 unique configurations across graphene, germanene, silicene, and stanene nanoribbons with varying geometries, impurity concentrations, and energy levels. A central contribution of this work is the development of a geometrydriven, physically interpretable feature space that enables the models to generalize across material types and device sizes. Random Forest regression and classification models are evaluated in terms of accuracy, stability, and extrapolation ability. Regression consistently outperforms classification in capturing continuous transport behavior on in-domain data. However, extrapolation performance degrades significantly, revealing the limitations of tree-based models in unseen regimes. This study highlights both the potential and constraints of scalable ML models for quantum transport prediction and motivates future research into physics-informed or graph-based learning architectures for improved generalization in spintronic and nanoelectronic device design.

cond-mat.mes-hall↗

arXiv2025

Geometry-Driven Charge and Spin Transport in $\beta12$ Borophene Quantum Dots

Theoretical research has been conducted to study how geometry affects charge and spin transport in $β\mathrm{12}$ borophene quantum dots, which are confined systems. The study examined two distinct central regions, which included a circular disc and a regular hexagonal area that connected to semi-infinite zigzag and armchair borophene nanoribbon leads. The system was described by a five-band tight-binding Hamiltonian parameterized using first-principles data, and the transport properties were calculated within the non-equilibrium Green's function framework. Spin resolved transmissions and spin polarization were computed for a range of lead widths and proximity-induced exchange field strengths. The analysis revealed distinct transport characteristics determined by geometry and edge configuration: armchair-connected structures exhibited broader and more stable fully spin-polarized windows compared with zigzag-connected counterparts. Furthermore, critical lead-width thresholds ($\approx 1.01$ nm for zigzag and $\approx 0.87$ nm for armchair) and a moderate exchange field above which complete spin filtering occurs were identified. The results highlight the strong influence of edge termination and confinement geometry on transport properties and provide useful design guidelines for developing borophene-based nanoscale spintronic devices.

cond-mat.mes-hall↗