Gabriel Kotliar talks with
ScienceWatch.com and answers a few questions about
this month's New Hot Paper in the field of
Article Title: Electronic structure calculations
with dynamical mean-field theory
Kotliar, G, et al.
Journal: REV MOD PHYS
Year: JUL-SEP 2006
* Rutgers State Univ, Dept Phys & Astron, POB 849,
Piscataway, NJ 08854 USA.
* Rutgers State Univ, Dept Phys & Astron, Piscataway,
NJ 08854 USA.
Why do you think your paper is highly
This paper is a comprehensive review of Dynamical Mean Field Theory (DMFT),
a new approach to predict the physical properties of “strongly
correlated materials” starting from first principles, namely without
using any empirical information.
Would you summarize the significance of your paper
in layman’s terms?
"The unique functionalities
of strongly correlated electron materials promise
to revolutionize the technology of the
Strongly correlated materials are systems displaying a wealth of remarkable
properties, ranging from high-temperature superconductivity (high-Tc),
giant changes in resistivity as a response of applied magnetic fields or
pressure, a high thermoelectric figure of merit, and coexistence of
ferromagnetism and ferrolectricity, to name a few. They have great
potential for practical applications, and at the same time pose an enormous
In weakly correlated electron systems, such as noble metals and
semiconductors, the electrons move freely as independent particles. This is
not the case in the strongly correlated materials, and DMFT is a technique
which was developed recently to describe the resulting properties. I was
one of the
recipients of the 2006 Agilent Technologies
Europhysics Prize for my significant contributions to this development.
Where do you see your research leading in the
The unique functionalities of strongly correlated electron materials
promise to revolutionize the technology of the future. DMFT has the
methodology to open the road towards rational material design using
strongly correlated electron systems. The idea here is to accelerate the
process of innovation and discovery using the theoretical guidance and
computational algorithms of DMFT.
Professor Gabriel Kotliar
Department of Physics & Astronomy
Rutgers, The State University of New Jersey
Piscataway, NJ, USA