Supervisor: Associate Professor Mojtaba Alaei

Abstract: 

Using a dataset of 600 ferromagnetic materials with known crystal structures from previous studies, the objective is to extract relevant physical descriptors from quantum mechanical (ab initio) calculations to predict the Curie temperature through machine learning (ML) techniques and feature engineering. The ultimate goal is to identify the most significant ab initio descriptors for accurately predicting the magnetic transition temperature.

Internship duration: from 2 months 

Internship start date: no earlier than April 1, 2026

Requirements for the candidate:

Knowledge of ab initio calculations, Experiences with a DFT code (such as Quantum-Espresso, VASP, ...), Basic knowledge of python programing and machine learning

Monthly compensation: not available

Contact person: Mojtaba Alaei m.alaei@skoltech.ru