Hossein Tahmasbi, Ph.D.

Computational materials scientist — machine learning interatomic potentials, benchmarking & validation, high-throughput DFT/ML workflows. Currently at CASUS–HZDR.

Research

aiida-gw

AiiDA plugin for automated CP2K GW workflows targeting 2D materials: single-point, relaxation, and G0W0 band-gap calculations with HPC/SLURM support.

PyFLAME

An automated workflow for developing neural network interatomic potentials with FLAME, minimizing human intervention in data generation and training.

FLAME

A library for atomistic modeling environments: neural network interatomic potentials, minima hopping structure prediction, and exploration of potential energy surfaces.