James Chelikowsky

About

James Chelikowsky is the W. A. "Tex" Moncrief, Jr. Chair of Computational Materials and a professor in the Departments of Physics, Chemical Engineering, Chemistry, and Biochemistry at the University of Texas at Austin. His research focuses on computational materials science, including quantum modeling for electronic materials, high-performance computing, and the optical and dielectric properties of semiconductors. Chelikowsky's work has significantly contributed to understanding surface and interfacial phenomena in solids, point and extended defects in electronic materials, pressure-induced amorphization in silicates and disordered systems, as well as clusters and nano-regime systems. He has also developed high-performance algorithms to predict material properties. He obtained his B.S. in physics from Kansas State University in 1970 and his Ph.D. in physics from the University of California at Berkeley in 1975. His postdoctoral work was performed at Bell Laboratories from 1976-1978. Chelikowsky has held academic positions at the University of Oregon and the University of Minnesota before joining the University of Texas at Austin in 2005. Throughout his career, he has been actively involved in professional societies such as the Materials Research Society and the American Physical Society, serving on various committees and holding leadership roles. He has received numerous awards and honors, including fellowships, the David Turnbull Lectureship Award, the Aneesur Rahman Prize,…

Research topics

• Computer Science
• Materials science
• Physics
• Nanotechnology
• Quantum mechanics
• Condensed matter physics
• Classical mechanics
• Discrete mathematics
• Algorithm
• Engineering physics

Selected publications

• Roadmap on electronic structure codes in the exascale era

  Modelling and Simulation in Materials Science and Engineering · 2023 · 72 citations

  Abstract Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry, and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to…

  PublisherOA PDFDOI

• Prediction of Intrinsic Ferroelectricity and Large Piezoelectricity in Monolayer Arsenic Chalcogenides

  Nano Letters · 2020 · 48 citations

  Senior authorCorresponding

  space group) with sizable piezoelectricity.

  DOI

• Accelerating Time-Dependent Density Functional Theory and GW Calculations for Molecules and Nanoclusters with Symmetry Adapted Interpolative Separable Density Fitting

  Journal of Chemical Theory and Computation · 2020 · 33 citations

  Senior authorCorresponding

  Computing integrals over orbital pairs is one of the most costly steps in many popular first-principles methods used by the quantum chemistry and condensed matter physics community. Here, we employ a recently proposed interpolative separable density fitting method (ISDF) to significantly reduce the cost of steps involving orbital pairs in linear response time-dependent density functional theory and GW calculations. In our implementation, we exploit the symmetry property of a system to effectivel…

  DOI

• Magnetic iron-cobalt silicides discovered using machine-learning

  Physical Review Materials · 2023-03-29 · 15 citations

  articleSenior author

  We employ machine-learning (ML) combined with first principles calculations to discover different rare-earth-free magnetic iron-cobalt silicide compounds. Deep machine-learning models are used to provide rapid screening of over 350 000 hypothetical structures to select a small fraction of promising structures and compositions for further studies by first-principles calculations. An adaptive genetic algorithm is used to search for lower energy structures based on the promising chemical compositio…

  PublisherDOI

• Efficient Full-Frequency GW Calculations Using a Lanczos Method

  Physical Review Letters · 2024-03-21 · 11 citations

  articleOpen accessSenior author

  The GW approximation is widely used for reliable and accurate modeling of single-particle excitations. It also serves as a starting point for many theoretical methods, such as its use in the Bethe-Salpeter equation (BSE) and dynamical mean-field theory. However, full-frequency GW calculations for large systems with hundreds of atoms remain computationally challenging, even after years of efforts to reduce the prefactor and improve scaling. We propose a method that reformulates the correlation pa…

  PublisherOA PDFDOI

Recent grants

• DMREF:SusChEM:Collaborative Research: Design and Synthesis of Novel Magnetic Materials

  NSF · $364k · 2014–2017

• DMREF:SusChEM:Collaborative Research: Design and Synthesis of Novel Magnetic Materials

  NSF · $550k · 2017–2022

• CDI-TYPE I-COLLABORATIVE Materials Informatics: Computational Tools for Discovery and Design

  NSF · $358k · 2009–2013

Frequent coauthors

• Marvin L. Cohen

  Lawrence Berkeley National Laboratory

  167 shared

• Steven G. Louie

  Lawrence Berkeley National Laboratory

  93 shared

• Murilo L. Tiago

  The University of Texas at Austin

  63 shared

• Serdar Öğüt

  University of Illinois Chicago

  51 shared

• Yousef Saad

  50 shared

• Tzu-Liang Chan

  University of Hong Kong

  47 shared

• Leeor Kronik

  Weizmann Institute of Science

  43 shared

• M. M. G. Alemany

  Universidade de Santiago de Compostela

  42 shared

Labs

• Jim Chelikowsky's LabPI

Education

• Ph.D., Physics

  The University of California at Berkeley

  1975

• B.S., Physics

  Kansas State University

  1970

Awards & honors

• John Simon Guggenheim Fellowship (1996)
• Fellow of the American Physical Society (1987)
• David Turnbull Lectureship Award from the Materials Research…
• David Adler Lectureship Award from the American Physical Soc…
• Fellow of the Materials Research Society (2011)

Similar researchers at University of Texas at Austin

• George Georgiouh-107
• Keith Johnstonh-106
• Hal Alperh-66