Dr. Alireza Ghasemifard

• Utilized AMS to build an ML interatomic potential for 2D MoS2, facilitating large-scale, DFT-accurate structural optimization prior to device modeling • Reduced atomic-scale simulation time by 99% using ML interatomic potentials • Validated ML potentials achieving 99% correlation with DFT data for high accuracy modeling • Applied scalable ML potentials to large systems (up to 100,000 atoms)

• Generated a large-scale training dataset of over 20,000 DFT calculations using the SCAN+rVV10 functional for high-accuracy materials parameterization • Leveraged this dataset to train and validate Graph Convolutional Neural Network (GCNN) models for optimizing semiconductor properties • Collaborated directly with SCM's software development team to integrate and test their new MLIP module in AMS

• Completed a comprehensive curriculum consisting of 24 hours of expert-led lectures and 48 hours of proven hands-on TCAD laboratory sessions • Power electronics device (PED) design and virtual manufacturing techniques utilizing industry-standard Synopsys TCAD tools • Deepened understanding of TC

I specialize in semiconductor device modeling and nanoelectronics. My research focuses on simulating the electronic and transport properties of semiconductors, using QuantumATK, AMS, and TCAD Sentaurus. Supervised by Prof. Thomas Heine at TUD and Dr. Agnieszka Kuc at CASUS, my PhD focuses on applying computational and data-driven methods to characterize and optimize materials for nanoelectronic devices. I am eager to contribute to the design and development of semiconductor devices.