The LSU Center for Computation & Technology is hosting the 17th annual Mardi Gras Conference, February 11-14, 2010, in Baton Rouge, Louisiana. The Mardi Gras conferences take place annually, concentrating each year on a different computational theme of current relevance. The 17th annual Mardi Gras Conference will bring together individuals working on one specific broad area of Computational Science: Computational Materials and Methods. The audience and cast of individual speakers will be a mix of computational material scientists and applied mathematicians.

The goal is to promote the cross-fertilization of ideas, foster information exchange, enable community building and expose graduate students and postdocs to the newest methods and advances. The conference will feature tutorial presentations, posters, in-depth discussions, and demonstrations in addition to several invited speakers. As always, the conference concludes with an afternoon trip to New Orleans for one of the city's largest Mardi Gras parades, Endymion.

NEWS

Conference lodging is now open for reservations.

Registration Closed

Submissions Closed

SCOPE

Solids with strong electronic correlations and materials with complex structure have the potential to produce novel devices and materials to fuel technological growth. Understanding these materials is a substantial challenge, since the paradigms of mean field properties, such as Fermi liquid theory and BCS superconductivity in the case of electronic materials, fail to describe the behavior of these materials. Also, understanding how complex structure predicts the material behavior, such as the case of protein structure or nanoparticles, is a challenge since these show unique sensitivity to form and size that cannot be understood from bulk properties. We have seen tremendous strides in computing power that have lead to far more accurate calculations of the properties of these classes of materials.

Large scale computation has been a major contributor to theoretical understanding of these systems, which in part is due to improvements in computing hardware. Far more important than the growth in raw computing power are the advances in computational algorithms that have enabled us to reorder the calculations to take advantage of the inherent speedups of parallel or distributed computing. A beginning graduate student or postdoc in computational materials science must learn not only about the physics of these materials, but also the computational algorithms that permit him or her to calculate their properties. Unfortunately, often the experts in each field do not interact as much as they could, and so advances in one area are not broadcast to workers in other disciplines.