Machine Learning for Materials Design
Research
Our research group uses advanced computational methods to design sustainable materials for diverse applications, including semiconductor devices, spintronics, neuromorphic devices, thermoelectrics, and energy storage devices. We use scalable machine learning frameworks to improve density functional theory simulations, connect microscopic and mesoscopic simulations, and model advanced material properties. Our techniques incorporate artificial intelligence to improve efficiency, enabling us to study phenomena as diverse as magnetostructural phase transitions in ultrafast magnetic memory devices and electron transport in nanoscale electronics.
Team
- Dr. Attila Cangi
- Bartosz Brzoza
- Lenz Fiedler
- Karan Shah
- Dr. Uwe Hernandez Acosta
- Dr. Timothy Callow
Research Topics
- Scalable machine learning for electronic structure calculations
We develop the Materials Learning Algorithms (MALA), a physics-informed machine learning framework that aims to accelerate conventional density functional theory simulations. Using neural networks, MALA efficiently computes the electronic structure of matter, enabling accurate determination of energies and forces that are critical for atomistic simulations.
- Magneto-structural phase transitions
We bridge the divide between atomistic simulations at the microscopic scale and material simulations at the mesoscopic scale.
- First-principles electronic transport properties
We use advanced simulation methods, such as time-dependent density functional theory, to model how electrons in materials respond to optical laser light. This enables us to predict important material properties, including response functions and electronic transport behavior, which are essential for designing next-generation photonic and nanoelectronic devices.
- Electronic structure methods and machine learning
We develop advanced electronic structure methods such as density functional theory (DFT) for modeling both static and dynamic material properties.
Events
- Theoretical characterization of electrode materials for next-generation batteries
Theoretical characterization of electrode materials for next-generation batteries
- Induced chirality in the condensed phase
Induced chirality in the condensed phase
- Many-particle systems under extreme conditions
Many-particle systems under extreme conditions
- Strongly Coupled Coulomb Systems (SCCS) 2022 Conference
Strongly Coupled Coulomb Systems (SCCS) 2022 Conference
