James Chelikowsky

James Chelikowsky is Professor and W. A. "Tex" Moncrief, Jr. Chair in Computational Materials at The University of Texas at Austin's Oden Institute for Computational Engineering and Sciences (ICES). His research pioneers quantum mechanical simulations for materials design and discovery across multiple domains.

His educational background includes:

• B.S. in Physics from Kansas State University (1970)
• Ph.D. in Physics from University of California at Berkeley (1975)

Chelikowsky's research spans computational materials science with focus on quantum models for functionalized nanostructures, simulations of liquids and crystal growth, "green magnetism" in dilute magnetic semiconductors, oxide defects, materials informatics, and high-performance electronic structure algorithms. His work bridges theoretical physics with practical materials engineering to solve complex problems in energy and electronics.

Analysis of his 2012-2022 publications reveals evolving focus from fundamental quantum simulations toward machine learning integration for magnetic materials discovery, while maintaining strong contributions to two-dimensional materials and interfacial phenomena. Key trends include increased computational complexity and interdisciplinary collaboration with experimental groups.

His distinguished honors include:

• Feynman Prize for Theory (2022)
• FMD John Bardeen Award (2021)
• Aneesur Rahman Prize (2013)
• Multiple society fellowships (MRS, AAAS, APS)
• Guggenheim Fellowship (1996)

As leader of an active research group, Chelikowsky mentors graduate students in computational methods development. While specific grant details aren't provided, his sustained publication record and named chair position indicate substantial ongoing research funding. His group maintains strong industry and national laboratory collaborations evident in co-authorship patterns.

The Computational Materials Group operates through ICES with research facilities supporting high-performance computing for materials simulations. Current projects focus on machine learning-guided materials discovery and quantum mechanical modeling of novel electronic materials.