Employing advanced ab initio computations to probe the fundamental electronic, structural, and kinetic properties governing energy materials performance.

Elucidating key atomistic mechanisms such as ion transport, charge carrier dynamics, and surface reactivity and establishing structure-property relationships to rationally design next-generation energy materials.

Accelerating materials discovery by integrating high-throughput computations with materials informatics and machine learning.

Developing predictive models and materials descriptors to efficiently screen vast materials spaces for candidates with targeted functionalities, leveraging large first-principles datasets.

Creating advanced computational methods and simulation tools tailored for quantum materials, particularly those with strong correlations.

Developing and implementing sophisticated many-body techniques, as well as exploring novel algorithms leveraging quantum computing approaches, to accurately model complex phenomena and dynamics.