Attila Cangi

Dr. Attila Cangi is the Head of Department for Machine Learning for Materials Design at the Center for Advanced Systems Understanding (CASUS), part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). His roles include leading research in computational materials science and developing scalable ML methods for electronic structure calculations. He has held permanent staff scientist positions at HZDR and Sandia National Laboratories, with postdoctoral experience at the Max Planck Institute. His research focuses on accelerating materials discovery through AI-driven simulations for energy storage, thermoelectrics, spintronics, and semiconductor modeling.

Education:

• Ph.D. in Chemistry (Chemical and Materials Physics), University of California, Irvine (2011)
• M.Sc. in Physics, Rutgers University (2006)

Research Interests: Dr. Cangi’s work integrates machine learning with first-principles simulations to model electronic structures, predict material properties (e.g., conductivity, magnetism), and simulate phase transitions. His lab leverages high-performance computing to address challenges in warm dense matter, plasma physics, and quantum transport phenomena. Key areas include:

• Development of physics-informed ML algorithms (e.g., MALA package)
• Electronic structure modeling at extreme conditions
• Design of sustainable materials for energy applications

Lab & Collaborations: The Machine Learning for Materials Design group collaborates on projects like the European XFEL and employs tools such as atoMEC for average-atom modeling. Their work bridges theory and experiment, enabling predictions for novel materials under extreme environments.