About me

Computational materials scientist working on ab initio simulations and machine learning for large-scale atomistic modeling.

My research focuses on 2D materials and energy-related systems, combining Density Functional Theory (DFT) with data-driven approaches to enable predictive materials design. I am currently developing machine learning methods for scalable simulations, including workflows for training interatomic potentials.

I have over 10 years of research experience, 9 years of teaching in higher education, and more than 30 publications in journals such as Nature Communications, Science Advances, Nano Letters, and Physical Review Letters.

Tools & methods: DFT (VASP, Quantum ESPRESSO, GPAW, Siesta), machine learning (PyTorch, scikit-learn, XGBoost), and high-performance computing with Python-based workflows.