6 papers
Scalable Machine Learning Models for Predicting Quantum Transport in Disordered 2D Hexagonal Materials
Seyed Mahdi Mastoor, Amirhossein Ahmadkhan Kordbacheh
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 (Ave…
Geometry-Driven Charge and Spin Transport in Borophene Quantum Dots
Seyed Mahdi Mastoor, Amirhossein Ahmadkhan Kordbacheh
Theoretical research has been conducted to study how geometry affects charge and spin transport in borophene quantum dots, which are confined systems. The study exa…
Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations
Sadra Saremi, Amirhossein Ahmadkhan Kordbacheh
High-intensity laser plasma interactions create complex computational problems because they involve both fluid and kinetic regimes, which need models that maintain physical precisi…
Topological Regularization for Force Prediction in Active Particle Suspension with EGNN and Persistent Homology
Sadra Saremi, Amirhossein Ahmadkhan Kordbacheh
Capturing the dynamics of active particles, i.e., small self-propelled agents that both deform and are deformed by a fluid in which they move is a formidable problem as it requires…
Multi-Scale Modeling and Predictive Control of Active Brownian Particles
Sadra Saremi, Amirhossein Ahmadkhan Kordbacheh
Active Brownian particles (ABPs) function as self-driving agents that display non-equilibrium behavior through their pairwise interactions which lead to phase separation and vortex…
Multi-Scale Deep Learning for Colon Histopathology: A Hybrid Graph-Transformer Approach
Sadra Saremi, Amirhossein Ahmadkhan Kordbacheh
Colon cancer also known as Colorectal cancer, is one of the most malignant types of cancer worldwide. Early-stage detection of colon cancer is highly crucial to prevent its deterio…